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HP0-A21 - NonStop Kernel Basics - Dump Information

Vendor : HP
Exam Code : HP0-A21
Exam Name : NonStop Kernel Basics
Questions and Answers : 71 Q & A
Updated On : November 16, 2018
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HP0-A21 Questions and Answers

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HP0-A21 NonStop Kernel Basics

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HP0-A21 exam Dumps Source : NonStop Kernel Basics

Test Code : HP0-A21
Test Name : NonStop Kernel Basics
Vendor Name : HP
Q&A : 71 Real Questions

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HP HP NonStop Kernel Basics

HP says Itanium, HP-UX now not dead yet | killexams.com Real Questions and Pass4sure dumps

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  • eventually week's red Hat Summit in Boston, Hewlett-Packard vice chairman for business-general Servers and software Scott Farrand become caught devoid of PR minders by ServerWatch's Sean Michael Kerner, and might have slipped off message somewhat. In a video interview, Farrand suggested that HP became moving its method for mission-crucial methods faraway from the Itanium processor and the HP-UX working device and toward x86-primarily based servers and purple Hat enterprise Linux (RHEL), via a challenge to bring business-vital functionality to the Linux operating system known as venture Dragon Hawk, itself a subset of HP's undertaking Odyssey.

    challenge Dragon Hawk is an effort to deliver the high-availability points of HP-UX, comparable to ServiceGuard (which has already been ported to Linux) to RHEL and the Intel x86 platform with a mixture of server firmware and application. Dragon Hawk servers will run RHEL 6 and provide the means to partition processors into up to 32 remoted digital machines—a technology pulled from HP-UX's procedure aid supervisor. Farrand said that HP become positioning Dragon Hawk as its future mission-essential platform. "We definitely guide (Itanium and HP-UX) and love all that, however going ahead our strategy for mission-important computing is shifting to an x86 world," Farrand told Kernel. "it's not by way of twist of fate that folks have de-dedicated to Itanium, specifically Oracle."

    HP vice president Scott Farrand, interviewed at pink Hat Summit through Sean Michael Kerner of ServerWatch

    seeing that HP is still looking ahead to judgement in its case against Oracle, that statement can also have made a few americans in HP's company essential programs unit choke on their morning coffee. And sources at HP say that Farrand drifted a little off-course in his comments. The enterprise's reliable line on mission Odyssey is that it is in parallel to and complementary to the enterprise's investments in Itanium and HP-UX. A source at HP pointed out Farrand overlooked part of HP's challenge Odyssey briefing notes to that effect: "assignment Odyssey contains persisted funding in our centered mission-crucial portfolio of Integrity, NonStop, HP-UX, OpenVMS in addition to our investments in constructing future mission-essential x86 systems. offering Serviceguard for Linux/x86 is a step toward attaining that mission-crucial x86 portfolio."

    challenge Odyssey, however, is HP's clear highway ahead with purchasers that have not bought into HP-UX during the past. without a assist for Itanium previous purple Hat Enterprse Linux edition 5, and with RHEL being increasingly valuable to HP's strategy for cloud computing (and, pending litigation, assist for Oracle on HP servers), in all probability Farrand become simply a little bit ahead of the business in his pronouncement.

    Tip of the hat to Ars reader Caveira for his tip on the ServerWatch story.

     

    Some general Kernel thoughts | killexams.com Real Questions and Pass4sure dumps

    This chapter is from the publication 

    The discussion of operating equipment internals presents many challenges and opportunities to an creator. Our method is to talk about each area of the kernel, believe the challenges confronted by kernel designers, after which explore the route taken towards the ultimate solution implemented in the HP-UX code.

    before we focus on HP-UX specifics, let's discuss some generic challenges confronted by using kernel designers. as with all programming project, there are commonly many different easy methods to approach and remedy an issue. every so often the decision is in line with the programmer's past experience, and infrequently it is dictated via the particular necessities of each kernel design characteristic. As an operating gadget matures, these individual point options are often modified or "tweaked" in order to tune a kernel's operating parameters and convey them into alignment with performance aims or equipment benchmark metrics. The HP-UX working equipment is the fabricated from a continuous growth technique that has enabled the refinement of core aspects and added many enhancements and capabilities over the years.

    Kernel statistics constructions

    Programmers commonly use algorithms or borrow coding ideas from a "bag-of-hints" that belongs to the software development community at huge. This standard capabilities base consists of many elements of special hobby to folks that craft working gadget code. Let's explore some of the challenges that kernel programmers face and try to strengthen a simple realizing of just a few of the standard programmatic solutions they employ.

    Static Lists (Static Tables)

    The kernel frequently should keep an intensive list of parameters involving some facts constitution or managed useful resource. The simplest way to shop this category of data is to create an ordered, static list of the attributes of each and every member of the listing.

    every information constitution is described in accordance with the particular person items of facts assigned to each element. once each and every parameter is typed, the dimension (in bytes) of the constitution is ordinary. These constructions are then kept in contiguous kernel area (as an array of constructions) and may be comfortably listed for quick access.

    As a familiar observation, and by using no capability a fixed rule, the naming conference of those lists may additionally resemble right here pattern (see determine 3-3). If the name of the facts structure for a single member of the record is described as data_t, then the kernel pointer to the birth of the checklist could be facts*. The checklist could also be referenced by way of an array named statistics[x], and the variety of points can be stored in ndata. Many examples within the kernel observe this conference, however by means of no means all of them.

    03fig03.giffigure 3-3. Tables and Lists

    --> professionals

    The house essential for a static list must be allocated all through system initialization and is regularly controlled by using a kernel-tunable parameter, which is determined prior to the building of the kernel picture. the first entry in a static information table has the index cost of 0, which allows easy calculation of the beginning handle of each factor inside the table (assuming a fixed size for every member point).

  • example

    count on that every records structure incorporates exactly sixty four bytes of facts and that the beginning of the static listing is described by the kernel image mylist. in case you wanted to entry the records for the list member with an index variety of 14, you could comfortably take the handle kept within the kernel pointer mylist* and add 14 × 64 to it to reach at the byte handle corresponding to the beginning of the fifteenth factor within the list (don't forget that the listing index starts with 0). If the structure is described as an array, you may simplify the entry by referencing mylist[14] in your code.

  • Cons

    The main drawback to this strategy is that the kernel should give satisfactory listing features for all advantage scenarios that it might encounter. Many system administrators are considered godlike, however very few are in reality clairvoyant! in the case of a static listing, the simplest manner for it to grow is for the kernel to be rebuilt and the equipment rebooted.

    another consideration is that the useful resource being managed ought to have an index number associated with it anyplace it needs to be referenced inside the kernel. while this may additionally look fundamental originally, consider about the scenarios of preliminary task, index number reuse, useful resource sharing and locking, and so forth.

    summary

    while this category of structure is traditionally one of the vital normal, its lack of dynamic sizing and requirement to devise for the worst case has put it on the hit list for a lot of kernel growth initiatives.

    Dynamic Linked Lists (Dynamic Tables)

    The individual elements of a list have to be maintained in a manner that allows for the kernel to computer screen and manipulate them. unlike the points in a static record, the entire points of a dynamic listing don't seem to be neatly grouped collectively in a contiguous memory space. Their particular person locations and relative order don't seem to be usual or predictable to the kernel (as the name "dynamic" suggests).

    it's a comparatively primary assignment so as to add facets to a listing as they are requested (offering the kernel has an efficient kernel reminiscence-management algorithm, which is discussed later). once a data structure has been allotted, it ought to be linked with other constructions of the same checklist. Linkage methods fluctuate in complexity and convenience.

    once a structure has been allocated and the suitable facts stored, the problem is in gaining access to the statistics in a well timed manner. a simple index will not suffice due to the noncontiguous nature of the individual checklist points. The option is to "walk" the checklist with the aid of following ahead pointers inserted into each and every list factor as a method of constructing a continual path during the checklist or to put into effect any other classification of index information structure or hash function. whereas a hash greatly reduces the access/search time, it is a calculated overhead and need to be used every time an merchandise within the list is required.

    An extra problem comes when it's time for the kernel to clear up a constitution that is not any longer necessary. If the individual aspects of the list have been easily linked by means of a single forward-linkage pointer, then the assignment of casting off a single element from the list will also be time drinking. The checklist element, which facets to the factor to be removed, must be identified with the intention to restoration the wreck within the chain that the elimination will trigger. These necessities result in the construction of bidirectional linkage schemes, which permit for quicker deletion but require further overhead all through setup and preservation.

    pros

    The main attraction to the dynamic listing is that the elements consumed by way of the list are most effective allocated as they are crucial. If the want arises for extra listing aspects, they're quite simply allocated on the fly, and a kernel rebuild and reboot aren't needed. additionally, when a list element isn't any longer mandatory, its house could be lower back to the kernel pool of purchasable reminiscence. This could reduce the standard dimension of the kernel, which might also positively affect performance on a gadget with tight memory measurement constraints.

    Cons

    Nothing is free when it involves programming code! The comfort of dynamic lists comes with several linked expenses. The kernel should have a good approach to allocate and reclaim reminiscence resources of varying sizes (different list structures have distinctive element measurement requirements).

    The challenge of the way to hyperlink particular person listing elements collectively increases the complexity and dimension of every facts constitution in the checklist (more choices to be evaluated via the kernel fashion designer!). The dynamic list creates extra challenges within the realm of browsing algorithms and indexing.

    abstract

    The latest movement is evidently toward a very dynamic kernel, which necessitates incorporation of an ever-increasing quantity and range of dynamic lists. The challenge for the modern kernel fashion designer is to help best the use and upkeep of dynamic lists. there is considerable probability right here to think outdoor the field and create enjoyable options to the indexing and linkage challenges.

    resource Allocation

    An early challenge for a kernel designer is to music the utilization of a device aid. The useful resource can be memory, disk house, or obtainable kernel statistics buildings themselves. Any item that can be used and reused all over the operational lifestyles cycle of the system have to to be tracked via the kernel.

    Bit Allocation Maps

    A bitmap is possibly probably the most easiest skill of maintaining track of aid usage. In a bitmap, every bit represents a fixed unit of the managed aid, and the state of the bit tracks its current availability.

    A useful resource should be quantified as a set unit size, and the good judgment of the bitmap must be described (does a 0 bit indicate an purchasable resource or a used useful resource?). once these floor guidelines have been decided, the map can be populated and maintained.

  • illustration

    In practices a aid bit map requires particularly low upkeep overhead. The exact measurement of the map will vary in line with the number of useful resource devices being mapped. because the unit size of the resource raises, the map turns into proportionally smaller, and vice versa. The measurement of the map comes into play when it is being searched: the bigger the map, the longer the hunt may also take. Let's anticipate that we have reserved a contiguous 32-KB block of kernel memory and we wish to shop statistics there in 32-byte constructions. It turns into a reasonably primary concern to allocate a 1024-bit bitmap structure (128 bytes) to song our resource's utilization. in the event you should locate an available storage location, you perform a sequential search of the bitmap except you find an available bit, set the bit to indicate that the house is now used, and translate its relative place to indicate the attainable 32-byte enviornment in the memory block.

  • pros

    The relative simplicity of the of the bitmap method makes it a beautiful first-circulate solution in lots of situations. A small map may well be used to tune a relatively tremendous aid. Most processors feature meeting language–stage bit-look at various, bit-set, and bit-clear functions that facilitate the manipulation of bitmaps.

    Cons

    because the size of the bitmap increases, the time spent finding an accessible aid also increases. If there's a necessity for sequential devices from the mapped space, the allocation algorithms turn into lots extra advanced. A useful resource map is a programmatic agreement and isn't a aid lock by means of any potential. A renegade component of kernel code, which ignores the bitmapping protocol, could comfortably compromise the integrity of the bitmap and the aid it manages.

    abstract

    If a gadget useful resource is of a static dimension and always utilized as a set-sized unit, then a bitmap may additionally show to be the most within your means administration system.

    aid Maps

    one other class of fastened resource mapping includes the utilization of a constitution called a aid map (see figure 3-4). here is a regular clarification of the strategy as there are lots of differing applications of this approach. within the case of a aid map, you have a aid of a set dimension in opposition t which individual allocations of varying sizes should be made.

  • instance

    For our example, let's agree with an easy message board. The message board has 20 purchasable lines for message display; every line has room for 20 characters. the whole aid has room for four hundred characters, however individual messages should be displayed on sequential strains. accept as true with posting the two following messages:

  • 03fig04.gifdetermine three-4. useful resource Maps

    To evade one of these situation, a resource map may well be employed to allocate sequential traces. every entry within the aid map would aspect to the next block of attainable line(s) on the board.

  • If the message board have been blank, then there could be just one entry in our aid map pointing to the first line and stating that 20 strains were sequentially available. To list the first message, we might allocate the primary three strains from the board, alter our aid map entry to aspect to the fourth line, and alter the count number to 17. to add the 2d message to the board, we would allocate two more strains and modify the primary entry within the map to aspect to the sixth line, with the count adjusted to fifteen.

  • In impact a useful resource map facets to the unused "holes" in the resource. The dimension of the resource block tracked through each map entry varies in accordance with utilization patterns.

    execs

    A resource map requires extraordinarily few exact entries to manipulate a large number of elements. If the allocation block dimension varies and needs to be contiguously assigned, then this may be your most fulfilling guess.

    Cons

    Map entries are always being inserted and deleted from the maps. This requires constant moving of the particular person map entries (the saving grace right here is that there are relatively few entries). one more difficulty is the dimension of the resource map itself: if you run out of entries, then freed supplies might also now not be accounted for and in impact could be lost (a kind of memory leak) to gadget usage except a reboot.

    abstract

    aid maps have long been utilized through system V Interprocess communique kernel capabilities, and if care is taken in their sizing, they are very effective.

    browsing Lists and Arrays

    where there are arrays and lists of information constructions, there is at all times a necessity to seek specific elements. in lots of instances, one statistics structure may have an easy pointer to connected structures, however there are times when all you comprehend is an attribute or attributes of a favored structure and never an specific handle.

    Hashtables: an alternative to Linear Searches

    looking a protracted list merchandise via item (frequently called a sequential or linear search) can be very time ingesting. To reduce the latency of such searches, hash lists are created. Hashes are a kind of indexing and might be used with both static arrays or linked lists to pace up the place of a particular aspect in the listing.

    to use a hash, a usual attribute of the item being searched for is used to calculate an offset into the hashtable (hash arrays are commonly sized to a power of two to aid in the calculation and truncation of the hashed value to suit the array measurement). The hashtable will comprise a pointer to a member of the listing that suits the hashed attribute. This entry can also or may additionally no longer be the actual entry you are looking for. if it is the item you are looking for, then your search is over; if not, then there can be a forward hash-chain pointer to one more member of the listing that shares the identical hash attribute (if one exists). during this method, you will need to follow the hash-chain pointers until you discover the appropriate entry. while you will still have to perform a search of one or extra linked objects, the size of your search can be abbreviated.

    The efficiency is dependent upon how evenly allotted the attribute used for the hash algorithm is in the contributors of the listing. another key factor is the average measurement of the hashtable.

  • instance

    feel you have got a list of your pals' names and speak to numbers. As you add names and numbers to the record, they are comfortably positioned in an attainable storage slot and not stored in any selected order. traditionally, you may kind the checklist alphabetically every time an entry is made, however this is able to require "reordering the deck." agree with as an alternative the usage of a hashtable.

    As each and every entry is made, the number of letters in every name is counted; if there are greater than 9, then simplest the closing digit of the count number is saved. A separate hashtable array with 10 entries is additionally maintained with a pointer to a name in the list with that hash count number. As each and every name is added to the listing, it is linked to all the different checklist participants with the identical hash count number. See determine 3-5.

    03fig05.giffigure three-5. Hashtables and Chains

  • if your checklist had one hundred names in it and your had been hunting for Fred Flintstone, the device would first total the persona count number (Fred has 4 and Flintstone has 10, a complete of 15 counting the area between the names), which would send us to hash[5]. This entry would aspect to a name whose hash count number is 5; if this had been not the Fred Flintstone entry, you might with no trouble follow the embedded hash-chain pointer except you discovered Fred's entry (or reached the end of the record and failed the search).

    If there have been a hundred entries within the table and 10 entries within the hashtable, the use of a standard distribution, then every hash chain would have 10 entries. On standard, you could possibly ought to comply with an individual chain for half of its length to get the facts you wanted. that might be a 5-linkage pointer search in our example. If we had to perform a sequential search on the unordered facts, the typical search size would have been 50 features! Even considering that the time required to perform the hash calculation, this might effect in considerable mark downs.

    while this instance is greatly simplified, it does display the basics of hash-headers and hash chains to speed up the vicinity of the "right" statistics structure in a randomly allotted facts desk.

    professionals

    Hashing algorithms offer a versatile indexing formula that isn't tied to fundamentals reminiscent of numerical sequence or alphabetic order. imperative attributes are used to calculate an offset into the hash-chain header array. The header elements to a linked list of all objects sharing the equal hash attribute, hence reducing the ordinary search time required to find a selected item within the checklist.

    Cons

    The certain attributes used for the hash can be a bit summary in thought and have to be carefully considered to assure that they aren't artificially influenced and do not result in uneven distributions to the particular person chains. If the size of the useful resource pool being hashed grows, the length of the particular person chains may also turn into excessively long and the payback could be diminished.

    whereas the primary thought of hashing is very essential, each and every implementation is according to specific attributes, some numeric, some personality-based, and so on. This requires the programmer to cautiously analyze the information sets and establish which attribute to use as a key for the hash. commonly, probably the most obtrusive one can also now not be the premiere one.

    summary

    Hashing is right here to dwell (at least for the foreseeable future). Make your peace with the theory, as you'll see quite a lot of implementations during all areas of kernel code.

    Binary Searches

    When it comes to shopping a hard and fast listing for a worth, there are many strategies. The brute-force formula is to quite simply delivery with the first point and proceed in a linear manner during the whole checklist. In concept, if there were 1024 entries in the list, the commonplace search time would be 512 tests (occasionally the merchandise you are searching for would be on the front of the record and infrequently toward the conclusion, so the average could be 1024/2 or 512).

    a further components involves using a binary search algorithm. The determination department employed through the algorithm is in line with a binary-conditional verify: the merchandise being confirmed is both too excessive or too low. to ensure that a binary search to work, the facts in the listing ought to be ordered in an increasing or decreasing order. If the facts is randomly dispensed, yet another category of search algorithm should be used.

    believe a 1024-point record. we would beginning the quest via testing the factor within the core of the checklist (point 512). reckoning on the result of this check, we would then determine either aspect 256 or 768. we'd retain halving the remaining list index offset except we found the preferred element.

    pros

    Following this formulation, the worst-case search length for our theoretical 1024-element record would be 10! examine this to 1024 for the brute-drive linear search method.

    Cons

    while the reduction within the number of individual comparisons is stunning, the primary record facets need to be ordered. The impact of this on list preservation (including objects to or doing away with them from the checklist) should not be underestimated. An unordered list may well be without problems managed by utilizing a simple free-listing pointer and an embedded linkage pointer between all of the unused elements of a listing. If the listing is ordered, then many of its individuals may need to be moved every time an item is added or removed from the list.

    abstract

    we have regarded most effective a really primary type of binary search. Kernels employ many adaptations on this theme, each and every tuned to match the needs of a particular structure.

    Partitioned Tables

    contemporary architectures latest kernel designers with many challenges; one is the mapping of resources (both contiguous and noncontiguous). trust the task of tracking the web page-frames of actual memory on a device. If actual memory is contiguous, then an easy usage map can be created, one entry per pageframe, the page number will be the identical because the index into the array.

    On modern telephone-oriented systems, there may be dissimilar memory controllers on separate busses. regularly, the hardware design dictates that each and every bus be assigned a component of the memory tackle area. This classification of tackle allocation may result in "holes" in the physical reminiscence map. the use of partitioned tables offers a method to efficaciously map round these holes.

  • illustration

    accept as true with the drastically simplified instance in determine 3-6. in an effort to control a aid of sixteen, items we may use an easy sixteen-element array (as proven on the left facet of the determine). in this illustration, there's a gap within the useful resource allotment; physically, the fifth through 14th aspects are missing. If we use the standard array method, sixteen features will nevertheless be mandatory in the array if we wish to keep the relationship between the array index and the corresponding handle of the useful resource.

    03fig06.gifdetermine three-6. Partitioned Tables

    via switching to a two-tier partitioned desk, we are able to map the materials on each side of the gap and in the reduction of the amount of table space vital. the first tier is a simple array of 4 features, each either a legitimate pointer to a block of statistics structures or a null pointer signifying that the linked block of materials doesn't exist.

    apart from the pointer, an offset value is stored in every point. here is used within the case where the hole extends partially right into a block's latitude (as does the ultimate pointer in our illustration). The offset allows for us to skip to the aspect containing the first legitimate records structure.

    Let's examine the hassle required to locate an information structure. in case you obligatory the assistance concerning the fifteenth aid and were using the basic array method, you might most effective must index into the fifteenth element of the array (facts[14]).

    If the partitioned strategy were getting used, you can first divide the element tackle via the dimension of the second-tier structures. For our example that might be 14/four, which would yield three with a the rest of two. you could then index into the first-tier array to the fourth element (index = three), observe the pointer found there, and use the remainder to offset into the partitioned table to the third element (index = 2).

  • In our simplified illustration, the one array strategy required room for 16 information structures even if there were best six materials being mapped. The partitioned strategy required room for less than eight records structures (in two partitioned tables of 4 points each and every) plus the very standard 4-element first-tier structure.

    at the start look, it could actually now not look that the payback is price the added effort of navigating two tables, but this is a extremely primary example. As we outlined prior, the method is used to manage tables tremendous ample to map all the physical web page-frames of a latest business server! There can also be thousands and thousands of talents pages desiring their personal statistics structures (and massive holes may exist). we will see partition tables in use once we focus on reminiscence administration.

    execs

    The value of partitioned tables is in the reduction of kernel memory usage. The less memory used through the kernel, the extra obtainable for person programs!

    Cons

    The components truly has very few drawbacks; the referencing of a map aspect is now a two-step procedure. The map point number must be divided by using the variety of elements in every partitioned desk structure (second-tier structure) to yield an index into the first-tier constitution. The the rest from this operation is the offset into the 2nd-tier constitution for the favored aspect. In follow, the partitioned tables are often sized to a power of two, which reduces the calculation of the offsets to basic bit-moving operations.

    summary

    Partitioned tables are dictated by way of the architecture and are a indispensable device in the kernel dressmaker's bag of tricks.

    The B-Tree Algorithm

    The b-tree is a a bit of advanced binary search mechanism that comprises making a collection of index structures arranged in a sort of relational tree. each constitution is known as bnode; the variety of bnodes depends on the variety of aspects being managed. the primary bnode is pointed to by using a broot constitution, which defines the width and depth of the standard tree.

    one of the most entertaining and robust features of the b-tree is that it may be extended on the fly to accommodate a metamorphosis within the variety of items being managed. B-bushes may well be used to map a small number of gadgets or tons of of lots through effortlessly adjusting the depth of the structure.

    The standard bnode includes an array of key-cost pairs. the important thing facts should be ordered in an ascending or descending method. To find a vital cost, a linear search is performed on the keys. This might also appear to be an old school approach, however let's consider what happens because the records set grows.

    the primary situation is the dimension of the bnode. A b-tree is declared to be of a particular order. The order is the variety of keys within the array constitution (minus 1—we are able to explain this as we talk about a simple illustration). If we've a third-order b-tree, then at most we would have three keys to investigate for each and every search. Of path, we may handiest reference three values with this basic structure!

    in order to grow the scope of our b-tree's usefulness, we have to develop the depth of the tree. as soon as a b-tree expands beyond its order, extra bnodes are created and the depth of the tree is elevated.

    best bnodes at the lowest stage of the tree comprise key-value information. The bnodes in any respect other degrees contain key-pointer records. This ability that with the intention to find a particular value, we have to conduct a search of a bnode at each and every degree of the tree. each and every search, on general, requires half as many examine operations as there are keys in the bnode (the order). This potential that the commonplace search length is defined as (order/2) × depth. Optimization is performed by way of adjusting each the order and the depth of the b-tree.

  • illustration: growing the b-tree

    From determine 3-7, trust a very primary illustration of a 3rd-order b-tree. The common bnode has keys: 1, 2, 3. every little thing matches in a single bnode, so the depth is effectively 1.

    03fig07.gifdetermine 3-7. B-bushes

    when we add a fourth key, four, to the tree, it fills the bnode to capability and causes the growth of the tree. If the number of keys in a bnode exceeds the order, then it is time to split the node and develop the tree.

    To grow this basic tree, we create two new bnode constructions and movement half of the present key–price pairs to each. word that the facts is packed into the primary two entries of each of the brand new bnodes, which allows for for room to grow.

    next, we have to modify the depth value in the broot facts structure that defines the tree. The normal bnode have to even be reconfigured. First, it is cleared, and a single secret is created.

    Let's take a closer look at the bnode structure. notice that there are truly greater cost slots than there are key slots. This enables for 2 pointers to be created relating to each and every key entry. The pointer down and to the left is used if you are trying to find a key of a lower cost. The pointer down and to the right is used in case you are seeking one it truly is more suitable than or equal to the important thing.

    Let's try finding a value given a key = 2:

    look for a key = 2. as a result of there is no ideal suit, we look for a key it's > 2 and follow the pointer down and to the left of that key to the subsequent bnode.

    look for a key = 2. A in shape right here will yield the acceptable cost. We comprehend that here's a price and not yet another pointer, given that the broot structure told us we had a depth of two!

  • observe that the important thing values do not need to be sequential and can be sparse as long as they are ordered. Searches on the bottom stage of the tree return either a value or a "no in shape" message.

    The seek a key at all times attempts to find a perfect suit yielding either a price or a pointer to the subsequent reduce degree. If no suit is discovered and we are not on the bottom level, the following logic is used. if your search key lays between two adjoining key entries, the pointer to the subsequent degree lies under and between them. A search key under the primary key entry makes use of the first pointer within the bnode. in case your search key's larger than the last valid key within the bnode, then the remaining legitimate pointer is used.

    execs

    B-bushes could be grown dynamically, whereas their desk maintenance is inconspicuous. This makes them top-rated for managing kernel buildings that change in measurement. Key values can be packed or sparse and introduced to the table at any time, proposing for a versatile scope.

    Cons

    one more advantage is that given a sufficiently sized order, a b-tree can also grow to manipulate a big variety of gadgets while keeping a reasonably consistent search time. trust a 15th-order b-tree: the primary depth would map 16 items, the second depth would map 256 items, and the third depth would yield a scope of 4096 whereas handiest requiring the hunt of three bnodes! This category of exponential boom makes it very standard for administration of small to colossal components.

    The b-tree, binary-tree, and balanced-tree belong to a family unit of connected search buildings. whereas the b-tree has a modest preservation overhead as a result of its fundamental exact-down pointer logic, its boom algorithm can result in carefully populated bnodes. This raises the variety of nodes required to map a given variety of information values. As with most methods, we trade table size for protection cost.

    an additional subject is that while the b-tree might also grow its depth to keep up with demand, it will probably now not alternate its order (without really reducing it down to the floor and rebuilding it from scratch). This capability that designers need to pay consideration to its skills usage when sizing the order in their implementations.

    summary

    The b-tree requires somewhat of analyze prior to its implementation but offers a very good formula for the mapping of ordered dynamic lists ranging in dimension from moderate to massive. we are able to see a realistic utility of the b-tree when we assess kernel administration of digital memory location constructions.

    Sparse Tables

    We mentioned the use of a hash to speed entry to participants of a static, unordered array. What would ensue if the hash measurement have been aggressively improved (even to the dimension of the array it referenced or greater)? at first you may suppose this could be a pretty good answer: readily create a hashing algorithm with sufficient scope, and lookups become a single-step technique. The issue is that the kernel information array and its corresponding hash might turn into prohibitively massive so as to guarantee a special reference.

    A compromise is to measurement the statistics constitution huge sufficient to grasp your worst-case element count number and hope that the hashing algorithm is thoroughly distributive in its nature. In a great circumstance, no two lively points would share the identical hash.

    in the less-than-highest quality actual world, there could be cases the place two information features do share a common hash. We may additionally solve the issue through dynamically allocating an further information constitution outdoor the fixed array and making a forward hash-chain hyperlink to it.

    continually, this step isn't vital if the hash formulation is sufficiently distributive. In practice, a ahead pointer may additionally simplest be vital in a very small percentage of the situations (lower than 1% or 2%). in the very infrequent case where a 3rd point should share the same hash, an extra structure can be chained to the 2nd, one etc (reference determine 3-eight).

    03fig08.gifdetermine 3-8. Sparse Tables

    execs

    Sparse lists tremendously in the reduction of the average search time to locate objects in unordered tables or lists.

    Cons

    Sparse lists require the kernel to manage the attainable sparse records-aspect areas as yet another kernel resource. As there's a chance that the statistics aspect first pointed to may additionally now not be the genuine one you are looking for, the target structure must comprise ample records to validate that it is or isn't the one you want. If it is never, a activities must be developed to "walk the chain."

    summary

    Spare lists work most useful when there is some regularly occurring attribute(s) of the favored data set that may be used to generate a sufficiently significant and distributive hash cost. the odds of desiring to create a forward chain pointer decrease significantly as the scope of the hash increases. we are able to see an example of this approach within the HP-UX kernel's digital-to-actual web page-frame mapping. In actual use, it is a one-in-a-million probability to discover a hash-chain with greater than three linked elements!

    The bypass listing

    in the world of search algorithms, the pass list is a brand new child on the block. Its use become first outlined in the Nineteen Nineties in a paper organized for the Communications of the affiliation for Computing equipment (CACM) by means of William Pugh of the school of Maryland. For additional information, consult with ftp://ftp.cs.umd.edu/pub/skipLists/skiplists.ps.Z.

    The algorithm can be employed to reduce search times for dynamic linked lists. The particular person list facets need to be assigned to the checklist in response to some ordered attribute. This strategy works neatly for linked lists with only a dozen or so members and equally as neatly for lists of several hundred members.

    firstly glance, bypass lists seem like effortlessly a series of forward- and reverse-linkage pointers. Upon closer examination, we see that some element at once to a neighbor, whereas others pass several in-between buildings. The surprising part is that the number of aspects skipped is the effect of random task of the pointer constructions to every record member because it is linked into the record.

    listing protection is fairly simple. to add a brand new member aspect, we quite simply bypass through the record unless we locate its neighboring individuals. We then quite simply hyperlink the new constitution between them. The removal of a member follows the same logic in reverse.

    When a new member is introduced, we need to make a decision at which tiers it will be linked. The implementation used in the HP-UX pregion lists makes use of a skip pointer array with four features. All contributors have a first-level forward pointer. the percentages are one in four that it'll have a 2nd-stage pointer, one in four of those may have a 3rd-stage pointer, and one in 4 of these can have a fourth-level pointer. As points could be introduced or faraway from the checklist at any time, the genuine distribution of the pointers takes on a pseudorandom nature.

    To facilitate the formulation, a symbolic first element is created, which all the time incorporates a ahead pointer at every of the skip tiers. It additionally shops a pointer to the highest lively level for the record. See determine three-9.

  • illustration

    From determine three-9, let's anticipate that we need to locate the structure with the attribute 9678. within the checklist nexus structure, we see that the highest degree lively pointer is at next[2], so we observe it. This structure has an attribute price of 5255, so we should proceed our search at this stage.

    We arrive lower back on the starting point constitution, so we back off to the 5255 constitution, drop down a level to next[1], and continue.

    We now arrive at the constitution with the 9678 attribute—or not it's a fit! Our search is over.

    in the illustration, it took simplest three searches. a simple binary search would have taken four searches.

  • 03fig09.giffigure 3-9. skip listing

    pros

    The pass record presents an interesting strategy for browsing that frequently outcomes in a discount of search instances when compared to a simple binary formula. adding and removing contributors to and from the list is reasonably brief.

    Cons

    It requires the advent of a multielement array for the ahead linkages. The random nature of the pointer project does not take into account the relative measurement or frequency of use of the a number of checklist elements. an often referenced constitution should be would becould very well be inefficiently mapped by way of the good fortune-of-the-draw (in our illustration we beat the binary components, however other members of our listing would now not: try searching for the 5015 constitution).

    abstract

    regardless of the random nature of this beast, the normal effect may be a web-sum benefit if the ratio between the number of items and the number of ranges is cautiously tuned.

    Operations Arrays

    contemporary kernels are often required to adapt to a variety of distinctive subsystems that may supply competing or alternate techniques to the same management task. A case in element is that of a kernel that needs to aid dissimilar file device implementations on the identical time.

    to achieve this aim, certain file techniques can be represented within the kernel via a digital object. virtual illustration masks all references to the actual object. here's all well and good, but what if kernel routines crucial to engage with the real object required code and helping statistics dependent upon type-selected attributes? An operations array, or vectored bounce table, may well be of service right here.

  • example

    agree with determine 3-10. right here we see an easy kernel desk with four features, each and every representing a member of a virtual checklist. each and every checklist member has its specific v_type registered, a sort-particular v_data[] array, and a pointer to a v_ops[] operational array.

    03fig10.giffigure 3-10. Operations Arrays: A Vectored start table

    For this model to work, the number of entries within the operational array and the functions they point to must be matched for each and every type the kernel is expected to tackle. In our illustration, there are 4 operational target features: open(), close(), examine(), and write(). at the moment, our gadget has best two adaptations labeled category: X and Y.

    When a hobbies is referred to as through a vectored leap referenced via v_ops[x], it's passed the address of the virtual objects v_data[] array. This allows the closing type-certain characteristic to work with a data set class that it knows.

    The fruits is that each one different kernel objects want simplest to request a name to v_ops[0] to instigate an open() of the digital object without subject or potential of whether it is of class X or Y. The operations array will address the redirection of the call. In follow, we are able to see many examples of this type of constitution in the kernel.

  • professionals

    The cost of redirecting a procedure call through a vector bounce desk is awfully low and for the most part transparent to all that use it.

    Cons

    In debugging, this is yet an additional stage of indirection to cope with.

    abstract

    The vectored bounce table, or operational array, provides a extremely advantageous abstraction layer between type-specific, kernel-resident functions, and other kernel subsystems.


    Works on My computer | killexams.com Real Questions and Pass4sure dumps

    one of the crucial insidious boundaries to continual start (and to continuous flow in application beginning generally) is the works-on-my-computing device phenomenon. any person who has worked on a application construction group or an infrastructure aid crew has experienced it. any person who works with such teams has heard the phrase spoken during (attempted) demos. The concern is so general there’s even a badge for it:

    possibly you've got earned this badge your self. I even have a few. you should see my trophy room.

    There’s a longstanding way of life on Agile groups that can also have originated at ThoughtWorks around the flip of the century. It goes like this: When a person violates the ancient engineering precept, “Don’t do the rest dull on intention,” they should pay a penalty. The penalty should be would becould very well be to drop a greenback into the team snack jar, or something lots worse (for an introverted technical classification), like standing in entrance of the team and singing a tune. To explain a failed demo with a glib “<shrug>Works on my machine!</shrug>” qualifies.

    it can not be feasible to evade the problem in all cases. As Forrest Gump talked about…smartly, you recognize what he stated. but we will minimize the issue with the aid of paying attention to just a few evident issues. (yes, I consider “obtrusive” is a note to be used advisedly.)

    Pitfall #1: Leftover Configuration

    issue: Leftover configuration from previous work enables the code to work on the construction atmosphere (and maybe the verify environment, too) while it fails on other environments.

    Pitfall #2: construction/check Configuration Differs From production

    The options to this pitfall are so akin to those for Pitfall #1 that I’m going to neighborhood both.

    answer (tl;dr): Don’t reuse environments.

    standard situation: Many developers installation an ambiance they like on their laptop/laptop or on the group’s shared construction environment. The environment grows from mission to mission as extra libraries are added and extra configuration alternatives are set. sometimes, the configurations conflict with one a further, and groups/individuals often make guide configuration alterations counting on which task is energetic at the moment.

    It doesn’t take lengthy for the development configuration to turn into very diverse from the configuration of the target production atmosphere. Libraries which are current on the development equipment might also no longer exist on the construction system. You may additionally run your local assessments assuming you’ve configured issues the identical as production only to discover later that you just’ve been the use of a different version of a key library than the one in creation.

    subtle and unpredictable adjustments in behavior turn up across development, verify, and creation environments. The situation creates challenges no longer simplest throughout construction but also all over creation assist work when we’re making an attempt to breed stated behavior.

    answer (lengthy): Create an remoted, dedicated construction environment for every challenge.

    There’s more than one useful strategy. which you can likely consider of a number of. here are a few percentages:

  • Provision a new VM (in the community, in your computing device) for each and every project. (I needed to add “locally, to your machine” because I’ve learned that in lots of larger agencies, developers should start through bureaucratic hoops to get access to a VM, and VMs are managed entirely with the aid of a separate purposeful silo. Go determine.)
  • Do your building in an remoted atmosphere (together with testing within the reduce stages of the test automation pyramid), like Docker or equivalent.
  • Do your building on a cloud-based mostly development environment it really is provisioned with the aid of the cloud issuer for those who outline a brand new venture.
  • install your continuous Integration (CI) pipeline to provision a fresh VM for each and every build/examine run, to be sure nothing should be left over from the final construct that might pollute the effects of the existing build.
  • installation your continual beginning (CD) pipeline to provision a clean execution ambiance for better-level trying out and for production, in preference to merchandising code and configuration info into an current ambiance (for a similar intent). notice that this strategy additionally offers you the capabilities of linting, trend-checking, and validating the provisioning scripts within the average course of a construct/installation cycle. easy.
  • All those alternate options gained’t be possible for every imaginable platform or stack. select and judge, and roll your personal as applicable. In widespread, all this stuff are fairly easy to do if you’re engaged on Linux. All of them may also be carried out for other *nix programs with some effort. Most of them are moderately effortless to do with windows; the most effective concern there is licensing, and if your company has an commercial enterprise license, you’re all set. For other systems, reminiscent of IBM zOS or HP NonStop, are expecting to do some hand-rolling of tools.

    the rest that’s feasible to your circumstance and that helps you isolate your building and test environments might be valuable. in case you can’t do all this stuff to your circumstance, don’t be troubled about it. just do what that you would be able to do.

    Provision a new VM in the community

    in case you’re engaged on a computer, computer, or shared building server running Linux, FreeBSD, Solaris, windows, or OSX, then you definitely’re in decent form. you could use virtualization utility corresponding to VirtualBox or VMware to get up and tear down local VMs at will. For the much less-mainstream platforms, you can also ought to build the virtualization tool from source.

    One issue I continually suggest is that developers cultivate an attitude of laziness in themselves. well, the right form of laziness, it's. You shouldn’t feel perfectly chuffed provisioning a server manually more than once. take some time all the way through that first provisioning recreation to script the things you discover alongside the manner. then you definately received’t must be aware them and repeat the same mis-steps again. (neatly, until you have fun with that kind of issue, of route.)

    for instance, listed here are a number of provisioning scripts that I’ve get a hold of once I obligatory to install development environments. These are all based on Ubuntu Linux and written in Bash. I don’t recognize in the event that they’ll help you, but they work on my laptop.

    if your company is operating RedHat Linux in production, you’ll likely are looking to modify these scripts to run on CentOS or Fedora, so that your development environments can be fairly close to the target environments. No massive deal.

    if you are looking to be even lazier, that you would be able to use a tool like Vagrant to simplify the configuration definitions to your VMs.

    yet another element: something scripts you write and whatever thing definition information you write for provisioning tools, hold them under edition control along with every assignment. be sure something is in version manage for a given assignment is every little thing indispensable to work on that mission…code, tests, documentation, scripts…every thing. this is fairly essential, I believe.

    Do Your construction in a Container

    a technique of isolating your development ambiance is to run it in a container. lots of the equipment you’ll examine for those who seek suggestions about containers are basically orchestration equipment meant to support us manage varied containers, customarily in a construction atmosphere. For native construction purposes, you definitely don’t want that an awful lot performance. There are a few functional containers for this intention:

    These are Linux-based mostly. even if it’s functional for you to containerize your building atmosphere is dependent upon what technologies you need. To containerize a building atmosphere for one more OS, corresponding to windows, may now not be worth the effort over simply working a full-blown VM. For other systems, it’s probably impossible to containerize a building ambiance.

    enhance in the Cloud

    this is a comparatively new choice, and it’s feasible for a restrained set of technologies. The skills over building a native building atmosphere is so you might stand up a sparkling atmosphere for each and every assignment, guaranteeing you won’t have any accessories or configuration settings left over from previous work. here are a couple of options:

    are expecting to look these environments increase, and predict to peer greater players in this market. assess which applied sciences and languages are supported so see even if one of those may be a healthy to your wants. on account of the rapid tempo of trade, there’s no sense in checklist what’s purchasable as of the date of this text.

    Generate test Environments on the Fly as a part of Your CI construct

    once you have a script that spins up a VM or configures a container, it’s convenient so as to add it to your CI construct. The competencies is that your exams will run on a pristine atmosphere, and not using a possibility of false positives as a result of leftover configurations from outdated models of the software or from different functions that had up to now shared the equal static verify environment, or as a result of look at various statistics modified in a outdated verify run.

    Many americans have scripts that they’ve hacked as much as simplify their lives, but they may additionally no longer be correct for unattended execution. Your scripts (or the equipment you utilize to interpret declarative configuration requirements) should be capable of run with out issuing any prompts (equivalent to prompting for an administrator password). They also deserve to be idempotent (this is, it received’t do any damage to run them distinct instances, in the case of restarts). Any runtime values that need to be provided to the script must be available by using the script as it runs, and not require any manual “tweaking” in advance of every run.

    The idea of “generating an environment” may sound infeasible for some stacks. Take the suggestion greatly. For a Linux atmosphere, it’s relatively commonplace to create a VM whenever you need one. For different environments, you can also not be able to do just that, but there may well be some steps you can take based on the usual concept of developing an ambiance on the fly.

    as an example, a group working on a CICS application on an IBM mainframe can define and begin a CICS atmosphere any time by way of running it as a common job. in the early 1980s, we used to do that robotically. as the Nineteen Eighties dragged on (and persisted during the 1990s and 2000s, in some companies), the world of corporate IT became more and more bureaucratized until this ability changed into taken out of builders’ arms.

    surprisingly, as of 2017 only a few construction groups have the alternative to run their own CICS environments for experimentation, development, and initial checking out. I say “unusually” as a result of so many other points of our working lives have more desirable dramatically, whereas that point seems to have moved in retrograde. We don’t have such issues engaged on the front conclusion of our functions, however when we circulation to the returned conclusion we fall via a kind of time warp.

    From a simply technical element of view, there’s nothing to cease a development crew from doing this. It qualifies as “producing an ambiance,” in my opinion. that you may’t run a CICS device “in the cloud” or “on a VM” (at least, not as of 2017), however you can follow “cloud pondering” to the challenge of managing your elements.

    in a similar fashion, that you could observe “cloud thinking” to different resources on your atmosphere, as smartly. Use your imagination and creativity. Isn’t that why you chose this container of labor, after all?

    Generate construction Environments on the Fly as part of Your CD Pipeline

    This suggestion is fairly a great deal the equal as the outdated one, except that it occurs later in the CI/CD pipeline. after getting some form of automated deployment in place, you could prolong that system to consist of instantly spinning up VMs or instantly reloading and provisioning hardware servers as part of the deployment method. At that factor, “deployment” actually capacity growing and provisioning the target environment, as adverse to moving code into an existing ambiance.

    This approach solves a couple of complications past fundamental configuration transformations. as an instance, if a hacker has delivered anything else to the construction ambiance, rebuilding that atmosphere out-of-supply that you manage eliminates that malware. individuals are discovering there’s value in rebuilding creation machines and VMs often despite the fact that there are no changes to “deploy,” for that intent as well as to prevent “configuration glide” that happens once we practice adjustments over time to an extended-running instance.

    Many businesses run home windows servers in production, principally to aid third-birthday celebration programs that require that OS. a controversy with deploying to an present windows server is that many functions require an installer to be latest on the target illustration. frequently, advice protection americans frown on having installers accessible on any construction illustration. (FWIW, I believe them.)

    if you create a home windows VM or provision a home windows server on the fly from controlled sources, then you definately don’t need the installer as soon as the provisioning is finished. You won’t re-deploy an utility; if a metamorphosis is critical, you’ll rebuild the total instance. that you can prepare the ambiance before it’s accessible in creation, and then delete any installers that have been used to provision it. So, this approach addresses more than just the works-on-my-computer issue.

    When it involves again-end programs like zOS, you gained’t be spinning up your personal CICS regions and LPARs for construction deployment. The “cloud thinking” if so is to have two identical production environments. Deployment then becomes a count of switching site visitors between the two environments, rather than migrating code. This makes it less demanding to put into effect creation releases devoid of impacting valued clientele. It additionally helps alleviate the works-on-my-machine difficulty, as trying out late in the birth cycle occurs on a real production atmosphere (although purchasers aren’t pointed to it yet).

    The general objection to here is the cost (it truly is, costs paid to IBM) to guide twin environments. This objection is continually raised through individuals who haven't fully analyzed the expenses of all of the prolong and transform inherent in doing issues the “historical method.”

    Pitfall #3: disagreeable Surprises When Code Is Merged

    issue: different groups and people address code determine-out and verify-in in a considerable number of ways. Some checkout code once and regulate it during the direction of a assignment, probably over a length of weeks or months. Others commit small adjustments generally, updating their local replica and committing changes many times per day. Most teams fall somewhere between those extremes.

    frequently, the longer you maintain code checked out and the more adjustments you make to it, the enhanced the possibilities of a collision in the event you merge. It’s also probably that you'll have forgotten exactly why you made each little trade, and so will the different americans who have modified the same chunks of code. Merges can be a trouble.

    throughout these merge events, all other value-add work stops. every person is trying to determine a way to merge the alterations. Tempers flare. everybody can claim, precisely, that the equipment works on their machine.

    solution: a simple way to keep away from this kind of factor is to commit small adjustments commonly, run the verify suite with everybody’s changes in region, and take care of minor collisions right away earlier than memory fades. It’s appreciably much less annoying.

    The better part is you don’t want any particular tooling to try this. It’s just a question of self-self-discipline. having said that, it best takes one individual who maintains code checked out for a long time to mess every person else up. Be aware about that, and kindly help your colleagues establish decent habits.

    Pitfall #four: Integration blunders discovered Late

    issue: This issue is comparable to Pitfall #3, however one level of abstraction higher. even though a group commits small alterations commonly and runs a complete suite of automated tests with every commit, they might also journey gigantic considerations integrating their code with other add-ons of the answer, or interacting with other purposes in context.

    The code might also work on my desktop, as well as on my team’s integration test ambiance, but as soon as we take the next step forward, all hell breaks free.

    solution: There are a few options to this difficulty. the first is static code analysis. It’s becoming the norm for a continual integration pipeline to encompass static code analysis as part of every construct. This occurs before the code is compiled. Static code analysis equipment investigate the source code as text, hunting for patterns which are commonplace to outcome in integration mistakes (among other issues).

    Static code analysis can discover structural problems within the code reminiscent of cyclic dependencies and high cyclomatic complexity, in addition to other simple complications like lifeless code and violations of coding requisites that tend to boost cruft in a codebase. It’s just the kind of cruft that reasons merge hassles, too.

    A connected advice is to take any warning stage mistakes from static code evaluation equipment and from compilers as real blunders. collecting warning stage mistakes is a very good strategy to emerge as with mysterious, sudden behaviors at runtime.

    The second solution is to integrate add-ons and run automated integration look at various suites generally. installation the CI pipeline so that when all unit-stage checks move, then integration-level exams are performed automatically. Let disasters at that degree wreck the construct, just as you do with the unit-degree assessments.

    With these two methods, that you can detect integration blunders as early as possible in the beginning pipeline. The past you discover an issue, the more convenient it's to repair.

    Pitfall #5: Deployments Are Nightmarish All-evening Marathons

    issue: Circa 2017, it’s nevertheless commonplace to locate corporations where people have “unencumber parties” whenever they deploy code to production. release events are only like any-night frat events, best with out the enjoyable.

    The problem is that the first time purposes are executed in a creation-like environment is when they are finished in the precise creation environment. Many concerns only come into sight when the crew tries to set up to production.

    Of direction, there’s no time or finances allotted for that. individuals working in a rush may additionally get the gadget up-and-running come what may, but frequently on the can charge of regressions that pop up later within the type of creation guide considerations.

    And it’s all as a result of, at every stage of the birth pipeline, the gadget “labored on my laptop,” whether a developer’s computing device, a shared verify atmosphere configured differently from construction, or some other unreliable atmosphere.

    answer: The answer is to configure each environment all over the start pipeline as close to creation as feasible. the following are customary instructions that you just may wish to alter depending on local cases.

    when you've got a staging ambiance, instead of twin production environments, it is going to be configured with all interior interfaces reside and exterior interfaces stubbed, mocked, or virtualized. in spite of the fact that here is so far as you're taking the concept, it's going to likely dispose of the want for free up events. but when that you would be able to, it’s first rate to continue upstream in the pipeline, to reduce unexpected delays in promoting code along.

    test environments between development and staging may still be operating the same version of the OS and libraries as creation. They should still be isolated at the acceptable boundary in keeping with the scope of trying out to be carried out.

    at the beginning of the pipeline, if it’s possible, boost on the same OS and identical general configuration as creation. It’s doubtless you wouldn't have as a whole lot memory or as many processors as within the creation atmosphere. The construction atmosphere also shouldn't have any live interfaces; all dependencies external to the application might be faked.

    At a minimal, suit the OS and free up degree to production as carefully as you can. as an instance, in case you’ll be deploying to windows Server 2016, then use a home windows Server 2016 VM to run your quick CI build and unit test suite. windows Server 2016 is in accordance with NT 10, so do your development work on windows 10 since it’s also in line with NT 10. in a similar fashion, if the construction ambiance is windows Server 2008 R2 (in accordance with NT 6.1) then strengthen on windows 7 (additionally in line with NT 6.1). You received’t be in a position to get rid of every single configuration difference, however you could be in a position to stay away from the majority of incompatibilities.

    observe the equal rule of thumb for Linux targets and building programs. for instance, if you will installation to RHEL 7.3 (kernel version 3.10.x), then run unit assessments on the same OS if viable. in any other case, look for (or construct) a version of CentOS in accordance with the identical kernel version as your production RHEL (don’t anticipate). At a minimal, run unit checks on a Linux distro in line with the identical kernel edition because the target production illustration. Do your construction on CentOS or a Fedora-based distro to minimize inconsistencies with RHEL.

    in case you’re the usage of a dynamic infrastructure management method that contains building OS situations from source, then this difficulty becomes tons less difficult to manage. that you could construct your construction, check, and production environments from the same sources, assuring version consistency right through the delivery pipeline. however the reality is that only a few corporations are managing infrastructure during this means as of 2017. It’s greater likely that you just’ll configure and provision OS cases in keeping with a published ISO, after which installation applications from a private or public repo. You’ll should pay close attention to models.

    in case you’re doing building work in your personal desktop or computing device, and you’re the usage of a move-platform language (Ruby, Python, Java, and so on.), you could believe it doesn’t be counted which OS you employ. You could have a pleasant construction stack on home windows or OSX (or something) that you just’re at ease with. having said that, it’s a good suggestion to spin up a native VM working an OS that’s closer to the construction environment, just to steer clear of unexpected surprises.

    For embedded building the place the development processor is diverse from the target processor, consist of a compile step to your low-degree TDD cycle with the compiler options set for the goal platform. this can expose mistakes that don’t ensue if you collect for the development platform. from time to time the identical version of the identical library will reveal distinct behaviors when finished on distinctive processors.

    a different suggestion for embedded development is to constrain your construction environment to have the equal memory limits and other useful resource constraints because the target platform. that you could capture certain styles of error early by means of doing this.

    For one of the most older back end structures, it’s possible to do building and unit testing off-platform for comfort. fairly early within the start pipeline, you’ll want to upload your source to an environment on the target platform and construct and test there.

    for example, for a C++ application on, say, HP NonStop, it’s effortless to do TDD on something local environment you like (assuming that’s feasible for the classification of utility), using any compiler and a unit checking out framework like CppUnit.

    in a similar fashion, it’s handy to do COBOL building and unit checking out on a Linux instance the usage of GnuCOBOL; tons quicker and more straightforward than the use of OEDIT on-platform for quality-grained TDD.

    despite the fact, in these situations, the goal execution environment is very diverse from the development atmosphere. You’ll need to pastime the code on-platform early in the beginning pipeline to get rid of works-on-my-computing device surprises.

    summary

    The works-on-my-laptop issue is without doubt one of the leading causes of developer stress and misplaced time. The leading explanation for the works-on-my-machine difficulty is ameliorations in configuration across development, test, and creation environments.

    The primary suggestions is to keep away from configuration differences to the extent viable. Take pains to make certain all environments are as akin to production as is purposeful. Pay consideration to OS kernel versions, library types, API types, compiler versions, and the types of any domestic-grown utilities and libraries. When variations can’t be avoided, then make observe of them and treat them as dangers. Wrap them in check cases to supply early warning of any considerations.

    The 2nd advice is to automate as a lot trying out as possible at different levels of abstraction, merge code commonly, build the utility generally, run the automatic look at various suites often, install generally, and (the place possible) build the execution environment frequently. this may help you discover issues early, whereas essentially the most fresh adjustments are still clean for your mind, and whereas the considerations are nonetheless minor.

    Let’s repair the world so that the next era of application developers doesn’t consider the phrase, “Works on my machine.”


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    NEW ORLEANS, June 3, 1997 — Microsoft Corp. and DGM & S Telecom, a leading international supplier of telecommunications software used in network applications and systems for the evolving distributed intelligent network, have teamed up to bring to market signaling system 7 (SS7) products for the Microsoft® Windows NT® Server network operating system. DGM & S Telecom is porting its OMNI Soft Platform &#153; to Windows NT Server, allowing Windows NT Server to deliver services requiring SS7 communications. Microsoft is providing technical support for DGM & S to develop the OMNI Soft Platform and Windows NT Server-based product for the public network.

    The SS7 network is one of the most critical components of today’s telecommunications infrastructure. In addition to providing for basic call control, SS7 has allowed carriers to provide a large and growing number of new services. Microsoft and DGM & S are working on signaling network elements based on Windows NT Server for hosting telephony services within the public network. The result of this collaborative effort will be increased service revenues and lowered costs for service providers, and greater flexibility and control for enterprises over their network service and management platforms via the easy-to-use yet powerful Windows NT Server environment.

    “Microsoft is excited about the opportunities that Windows NT Server and the OMNI Soft Platform will offer for telecom equipment suppliers and adjunct processor manufacturers, and for service providers to develop new SS7-based network services,” said Bill Anderson, director of telecom industry marketing at Microsoft. “Windows NT Server will thereby drive faster development, further innovation in service functionality and lower costs in the public network.”

    Microsoft’s collaboration with DGM & S Telecom is a key component of its strategy to bring to market platforms and products based on Microsoft Windows NT Server and independent software vendor applications for delivering and managing telecommunications services.

    Major hardware vendors, including Data General Corp. and Tandem Computers Inc., endorsed the OMNI Soft Platform and Windows NT Server solution.

    “With its high degree of availability and reliability, Data General’s AViiON server family is well-suited for the OMNI Soft Platform,” said David Ellenberger, vice president, corporate marketing for Data General. “As part of the strategic relationship we have established with DGM & S, we will support the OMNI Soft Platform on our Windows NT-compatible line of AViiON servers as an ideal solution for telecommunications companies and other large enterprises.”

    “Tandem remains the benchmark for performance and reliability in computing solutions for the communications marketplace,” said Eric L. Doggett, senior vice president, general manager, communications products group, Tandem Computers. “With Microsoft, Tandem continues to extend these fundamentals from our NonStop Kernel and UNIX system product families to our ServerNet technology-enabled Windows NT Servers. We are pleased that our key middleware partners such as DGM & S are embracing this strategy, laying the foundation for application developers to leverage the price/performance and reliability that Tandem and Microsoft bring to communications and the Windows NT operating system.”

    The OMNI Soft Platform from DGM & S Telecom is a family of software products that provide the SS7 components needed to build robust, high-performance network services and applications for use in wireline and wireless telecom signaling networks. OMNI Soft Platform offers a multiprotocol environment enabling true international operations with the coexistence of global SS7 variants. OMNI Soft Platform accelerates deployment of telecommunications applications so that service providers can respond to the ever-accelerating demands of the deregulated telecommunications industry.

    Programmable Network

    DGM & S Telecom foresees expanding market opportunity with the emergence of the “programmable network,” the convergence of network-based telephony and enterprise computing on the Internet.

    In the programmable network, gateways (offering signaling, provisioning and billing) will allow customers to interact more closely with, and benefit more from, the power of global signaling networks. These gateways will provide the channel to services deployed in customer premises equipment, including enterprise servers, PBXs, workstations, PCs, PDAs and smart phones.

    “The programmable network will be the end of one-size-fits-all service and will spawn a new industry dedicated to bringing the power of the general commercial computing industry to integrated telephony services,” said Seamus Gilchrist, DGM & S director of strategic initiatives. “Microsoft Windows NT Server is the key to future mass customization of network services via the DGM & S Telecom OMNI Soft Platform.”

    Wide Range of Service on OMNI

    A wide ranges of services can be provided on the OMNI Soft Platform, including wireless services, 800-number service, long-distance caller ID, credit card and transactional services, local number portability, computer telephony and mediated access. OMNI Soft Platform application programming interfaces (APIs) are found on the higher layers of the SS7 protocol stack. They include ISDN User Part (ISUP), Global System for Mobile Communications Mobile Application Part (GSM MAP), EIA/TIA Interim Standard 41 (IS-41 MAP), Advanced Intelligent Network (AIN) and Intelligent Network Application Part (INAP).

    The OMNI product family is

  • Global. OMNI provides standards-conformant SS7 protocol stacks. OMNI complies with ANSI, ITU-T, Japanese and Chinese standards in addition to the many other national variants needed to enter the global market.

  • Portable. Service applications are portable across the platforms supported by OMNI. A wide range of computing platforms running the Windows NT and UNIX operating systems is supported.

  • Robust. OMNI SignalWare APIs support the development of wireless, wireline, intelligent network, call processing and transaction-oriented network applications.

  • Flexible. OMNI supports the rapid creation of distributed services that operate on simplex or duplex hardware. It supports a loosely coupled, multiple computer environment. OMNI-Remote allows front-end systems that lack signaling capability to deploy services using the client/server model.

  • DGM & S Telecom is the leading international supplier of SignalWare &#153; , the telecommunications software used in network applications and systems for the evolving intelligent and programmable network. DGM & S Telecom is recognized for its technical innovations in high-performance, fault-resilient SS7 protocol platforms that enable high-availability, open applications and services for single- and multivendor environments. Founded in 1974, DGM & S Telecom offers leading-edge products and solutions that are deployed

    throughout North America, Europe and the Far East. DGM & S is a wholly-owned subsidiary of Comverse-Technology Inc. (NASDAQ “CMVT” ).

    Founded in 1975, Microsoft (NASDAQ “MSFT” ) is the worldwide leader in software for personal computers. The company offers a wide range of products and services for business and personal use, each designed with the mission of making it easier and more enjoyable for people to take advantage of the full power of personal computing every day.

    Microsoft and Windows NT are either registered trademarks or trademarks of Microsoft Corp. in the United States and/or other countries.

    OMNI Soft Platform and SignalWare are trademarks of DGM & S Telecom.

    Other product and company names herein may be trademarks of their respective owners.

    Note to editors : If you are interested in viewing additional information on Microsoft, please visit the Microsoft Web page http://www.microsoft.com/presspass/ on Microsoft’s corporate information pages. To view additional information on DGM & S, please visit the DGM & S Web page at (http://dgms.com/)


    IO Visor challenges Open vSwitch | killexams.com real questions and Pass4sure dumps

    Network functions virtualization (NFV) has enabled both agility and cost savings, triggering plenty of interest and activity in both the enterprise and service provider spaces. As the market begins to mature and organizations operationalize both NFV and software-defined networking (SDN), questions around nonstop operations arise. An area of recent focus is how do you provide nonstop operations during infrastructure code upgrades? The IO Visor Project claims it can implement nondisruptive upgrades unlike competitor Open vSwitch.  

    The fundamental challenge IO Visor tries to address is the operational impact of coupling input/output (I/O) with networking services. For example, if an OVS user wants to install a new version of OVS that adds packet inspection, a service disruption to the basic network I/O functionality is required.

    IO Visor claims to solve this problem by decoupling the I/O functionality from services. The IO Visor framework starts with the IO Visor Engine -- an in-kernel virtual machine (VM) that runs in Linux and provides the foundation of an extensible networking system. At the heart of the IO Visor Engine is Extended Berkley Packet Filter (eBPF). EBPF provides a foundation for developers to create in-kernel I/O modules and load and unload the modules without rebooting the host.

    It's worth noting that in-kernel I/O normally results in greater performance than solutions that run in user space. For example, the ability to run an IO Visor-based firewall should hypothetically offer performance increases over a firewall running in user space.

    Use case

    Is IO Visor in search of a problem that doesn’t exist, or are projects like this one the future of network function virtualization?

    The IO Visor project provided this use case: In a typical OVS environment today, updating the firewall function requires a restart of OVS or even a host reboot. Leveraging the IO Visor plug-in architecture, on the other hand, the in-kernel firewall plug-in would simply unload and reload. The bridging, router and Network Address Translation (NAT) functions would continue to operate.

    It’s early days for IO Visor, while OVS is mature and stable. Currently operational across thousands of environments, OVS provides carrier-grade performance. Most SDN users have reliably leveraged OVS and its extensive network of contributors and commercial products. In contrast, PLUMgrid is the only production-ready IO Visor-based platform I’m aware of.

    With all this said, I’m intrigued by the idea of abstracting I/O from network functions. The abstraction of I/O coupled with network function plug-ins adds flexibility to virtualized network architecture. I’ll be watching the project closely. What do you think: Is IO Visor in search of a problem that doesn’t exist, or are projects like this one the future of network function virtualization? 


    Works on My Machine | killexams.com real questions and Pass4sure dumps

    One of the most insidious obstacles to Continuous Delivery (and to continuous flow in software delivery generally) is the works-on-my-machine phenomenon. Anyone who has worked on a software development team or an infrastructure support team has experienced it. Anyone who works with such teams has heard the phrase spoken during (attempted) demos. The issue is so common there’s even a badge for it:

    Perhaps you have earned this badge yourself. I have several. You should see my trophy room.

    There’s a longstanding tradition on Agile teams that may have originated at ThoughtWorks around the turn of the century. It goes like this: When someone violates the ancient engineering principle, “Don’t do anything stupid on purpose,” they have to pay a penalty. The penalty might be to drop a dollar into the team snack jar, or something much worse (for an introverted technical type), like standing in front of the team and singing a song. To explain a failed demo with a glib “<shrug>Works on my machine!</shrug>” qualifies.

    It may not be possible to avoid the problem in all situations. As Forrest Gump said…well, you know what he said. But we can minimize the problem by paying attention to a few obvious things. (Yes, I understand “obvious” is a word to be used advisedly.)

    Pitfall #1: Leftover Configuration

    Problem: Leftover configuration from previous work enables the code to work on the development environment (and maybe the test environment, too) while it fails on other environments.

    Pitfall #2: Development/Test Configuration Differs From Production

    The solutions to this pitfall are so similar to those for Pitfall #1 that I’m going to group the two.

    Solution (tl;dr): Don’t reuse environments.

    Common situation: Many developers set up an environment they like on their laptop/desktop or on the team’s shared development environment. The environment grows from project to project as more libraries are added and more configuration options are set. Sometimes, the configurations conflict with one another, and teams/individuals often make manual configuration adjustments depending on which project is active at the moment.

    It doesn’t take long for the development configuration to become very different from the configuration of the target production environment. Libraries that are present on the development system may not exist on the production system. You may run your local tests assuming you’ve configured things the same as production only to discover later that you’ve been using a different version of a key library than the one in production.

    Subtle and unpredictable differences in behavior occur across development, test, and production environments. The situation creates challenges not only during development but also during production support work when we’re trying to reproduce reported behavior.

    Solution (long): Create an isolated, dedicated development environment for each project.

    There’s more than one practical approach. You can probably think of several. Here are a few possibilities:

  • Provision a new VM (locally, on your machine) for each project. (I had to add “locally, on your machine” because I’ve learned that in many larger organizations, developers must jump through bureaucratic hoops to get access to a VM, and VMs are managed solely by a separate functional silo. Go figure.)
  • Do your development in an isolated environment (including testing in the lower levels of the test automation pyramid), like Docker or similar.
  • Do your development on a cloud-based development environment that is provisioned by the cloud provider when you define a new project.
  • Set up your Continuous Integration (CI) pipeline to provision a fresh VM for each build/test run, to ensure nothing will be left over from the last build that might pollute the results of the current build.
  • Set up your Continuous Delivery (CD) pipeline to provision a fresh execution environment for higher-level testing and for production, rather than promoting code and configuration files into an existing environment (for the same reason). Note that this approach also gives you the advantage of linting, style-checking, and validating the provisioning scripts in the normal course of a build/deploy cycle. Convenient.
  • All those options won’t be feasible for every conceivable platform or stack. Pick and choose, and roll your own as appropriate. In general, all these things are pretty easy to do if you’re working on Linux. All of them can be done for other *nix systems with some effort. Most of them are reasonably easy to do with Windows; the only issue there is licensing, and if your company has an enterprise license, you’re all set. For other platforms, such as IBM zOS or HP NonStop, expect to do some hand-rolling of tools.

    Anything that’s feasible in your situation and that helps you isolate your development and test environments will be helpful. If you can’t do all these things in your situation, don’t worry about it. Just do what you can do.

    Provision a New VM Locally

    If you’re working on a desktop, laptop, or shared development server running Linux, FreeBSD, Solaris, Windows, or OSX, then you’re in good shape. You can use virtualization software such as VirtualBox or VMware to stand up and tear down local VMs at will. For the less-mainstream platforms, you may have to build the virtualization tool from source.

    One thing I usually recommend is that developers cultivate an attitude of laziness in themselves. Well, the right kind of laziness, that is. You shouldn’t feel perfectly happy provisioning a server manually more than once. Take the time during that first provisioning exercise to script the things you discover along the way. Then you won’t have to remember them and repeat the same mis-steps again. (Well, unless you enjoy that sort of thing, of course.)

    For example, here are a few provisioning scripts that I’ve come up with when I needed to set up development environments. These are all based on Ubuntu Linux and written in Bash. I don’t know if they’ll help you, but they work on my machine.

    If your company is running RedHat Linux in production, you’ll probably want to adjust these scripts to run on CentOS or Fedora, so that your development environments will be reasonably close to the target environments. No big deal.

    If you want to be even lazier, you can use a tool like Vagrant to simplify the configuration definitions for your VMs.

    One more thing: Whatever scripts you write and whatever definition files you write for provisioning tools, keep them under version control along with each project. Make sure whatever is in version control for a given project is everything necessary to work on that project…code, tests, documentation, scripts…everything. This is rather important, I think.

    Do Your Development in a Container

    One way of isolating your development environment is to run it in a container. Most of the tools you’ll read about when you search for information about containers are really orchestration tools intended to help us manage multiple containers, typically in a production environment. For local development purposes, you really don’t need that much functionality. There are a couple of practical containers for this purpose:

    These are Linux-based. Whether it’s practical for you to containerize your development environment depends on what technologies you need. To containerize a development environment for another OS, such as Windows, may not be worth the effort over just running a full-blown VM. For other platforms, it’s probably impossible to containerize a development environment.

    Develop in the Cloud

    This is a relatively new option, and it’s feasible for a limited set of technologies. The advantage over building a local development environment is that you can stand up a fresh environment for each project, guaranteeing you won’t have any components or configuration settings left over from previous work. Here are a couple of options:

    Expect to see these environments improve, and expect to see more players in this market. Check which technologies and languages are supported so see whether one of these will be a fit for your needs. Because of the rapid pace of change, there’s no sense in listing what’s available as of the date of this article.

    Generate Test Environments on the Fly as Part of Your CI Build

    Once you have a script that spins up a VM or configures a container, it’s easy to add it to your CI build. The advantage is that your tests will run on a pristine environment, with no chance of false positives due to leftover configurations from previous versions of the application or from other applications that had previously shared the same static test environment, or because of test data modified in a previous test run.

    Many people have scripts that they’ve hacked up to simplify their lives, but they may not be suitable for unattended execution. Your scripts (or the tools you use to interpret declarative configuration specifications) have to be able to run without issuing any prompts (such as prompting for an administrator password). They also need to be idempotent (that is, it won’t do any harm to run them multiple times, in the case of restarts). Any runtime values that must be provided to the script have to be obtainable by the script as it runs, and not require any manual “tweaking” prior to each run.

    The idea of “generating an environment” may sound infeasible for some stacks. Take the suggestion broadly. For a Linux environment, it’s pretty common to create a VM whenever you need one. For other environments, you may not be able to do exactly that, but there may be some steps you can take based on the general notion of creating an environment on the fly.

    For example, a team working on a CICS application on an IBM mainframe can define and start a CICS environment any time by running it as a standard job. In the early 1980s, we used to do that routinely. As the 1980s dragged on (and continued through the 1990s and 2000s, in some organizations), the world of corporate IT became increasingly bureaucratized until this capability was taken out of developers’ hands.

    Strangely, as of 2017 very few development teams have the option to run their own CICS environments for experimentation, development, and initial testing. I say “strangely” because so many other aspects of our working lives have improved dramatically, while that aspect seems to have moved in retrograde. We don’t have such problems working on the front end of our applications, but when we move to the back end we fall through a sort of time warp.

    From a purely technical point of view, there’s nothing to stop a development team from doing this. It qualifies as “generating an environment,” in my view. You can’t run a CICS system “in the cloud” or “on a VM” (at least, not as of 2017), but you can apply “cloud thinking” to the challenge of managing your resources.

    Similarly, you can apply “cloud thinking” to other resources in your environment, as well. Use your imagination and creativity. Isn’t that why you chose this field of work, after all?

    Generate Production Environments on the Fly as Part of Your CD Pipeline

    This suggestion is pretty much the same as the previous one, except that it occurs later in the CI/CD pipeline. Once you have some form of automated deployment in place, you can extend that process to include automatically spinning up VMs or automatically reloading and provisioning hardware servers as part of the deployment process. At that point, “deployment” really means creating and provisioning the target environment, as opposed to moving code into an existing environment.

    This approach solves a number of problems beyond simple configuration differences. For instance, if a hacker has introduced anything to the production environment, rebuilding that environment out-of-source that you control eliminates that malware. People are discovering there’s value in rebuilding production machines and VMs frequently even if there are no changes to “deploy,” for that reason as well as to avoid “configuration drift” that occurs when we apply changes over time to a long-running instance.

    Many organizations run Windows servers in production, mainly to support third-party packages that require that OS. An issue with deploying to an existing Windows server is that many applications require an installer to be present on the target instance. Generally, information security people frown on having installers available on any production instance. (FWIW, I agree with them.)

    If you create a Windows VM or provision a Windows server on the fly from controlled sources, then you don’t need the installer once the provisioning is complete. You won’t re-install an application; if a change is necessary, you’ll rebuild the entire instance. You can prepare the environment before it’s accessible in production, and then delete any installers that were used to provision it. So, this approach addresses more than just the works-on-my-machine problem.

    When it comes to back-end systems like zOS, you won’t be spinning up your own CICS regions and LPARs for production deployment. The “cloud thinking” in that case is to have two identical production environments. Deployment then becomes a matter of switching traffic between the two environments, rather than migrating code. This makes it easier to implement production releases without impacting customers. It also helps alleviate the works-on-my-machine problem, as testing late in the delivery cycle occurs on a real production environment (even if customers aren’t pointed to it yet).

    The usual objection to this is the cost (that is, fees paid to IBM) to support twin environments. This objection is usually raised by people who have not fully analyzed the costs of all the delay and rework inherent in doing things the “old way.”

    Pitfall #3: Unpleasant Surprises When Code Is Merged

    Problem: Different teams and individuals handle code check-out and check-in in various ways. Some checkout code once and modify it throughout the course of a project, possibly over a period of weeks or months. Others commit small changes frequently, updating their local copy and committing changes many times per day. Most teams fall somewhere between those extremes.

    Generally, the longer you keep code checked out and the more changes you make to it, the greater the chances of a collision when you merge. It’s also likely that you will have forgotten exactly why you made every little change, and so will the other people who have modified the same chunks of code. Merges can be a hassle.

    During these merge events, all other value-add work stops. Everyone is trying to figure out how to merge the changes. Tempers flare. Everyone can claim, accurately, that the system works on their machine.

    Solution: A simple way to avoid this sort of thing is to commit small changes frequently, run the test suite with everyone’s changes in place, and deal with minor collisions quickly before memory fades. It’s substantially less stressful.

    The best part is you don’t need any special tooling to do this. It’s just a question of self-discipline. On the other hand, it only takes one individual who keeps code checked out for a long time to mess everyone else up. Be aware of that, and kindly help your colleagues establish good habits.

    Pitfall #4: Integration Errors Discovered Late

    Problem: This problem is similar to Pitfall #3, but one level of abstraction higher. Even if a team commits small changes frequently and runs a comprehensive suite of automated tests with every commit, they may experience significant issues integrating their code with other components of the solution, or interacting with other applications in context.

    The code may work on my machine, as well as on my team’s integration test environment, but as soon as we take the next step forward, all hell breaks loose.

    Solution: There are a couple of solutions to this problem. The first is static code analysis. It’s becoming the norm for a continuous integration pipeline to include static code analysis as part of every build. This occurs before the code is compiled. Static code analysis tools examine the source code as text, looking for patterns that are known to result in integration errors (among other things).

    Static code analysis can detect structural problems in the code such as cyclic dependencies and high cyclomatic complexity, as well as other basic problems like dead code and violations of coding standards that tend to increase cruft in a codebase. It’s just the sort of cruft that causes merge hassles, too.

    A related suggestion is to take any warning level errors from static code analysis tools and from compilers as real errors. Accumulating warning level errors is a great way to end up with mysterious, unexpected behaviors at runtime.

    The second solution is to integrate components and run automated integration test suites frequently. Set up the CI pipeline so that when all unit-level checks pass, then integration-level checks are executed automatically. Let failures at that level break the build, just as you do with the unit-level checks.

    With these two methods, you can detect integration errors as early as possible in the delivery pipeline. The earlier you detect a problem, the easier it is to fix.

    Pitfall #5: Deployments Are Nightmarish All-Night Marathons

    Problem: Circa 2017, it’s still common to find organizations where people have “release parties” whenever they deploy code to production. Release parties are just like all-night frat parties, only without the fun.

    The problem is that the first time applications are executed in a production-like environment is when they are executed in the real production environment. Many issues only become visible when the team tries to deploy to production.

    Of course, there’s no time or budget allocated for that. People working in a rush may get the system up-and-running somehow, but often at the cost of regressions that pop up later in the form of production support issues.

    And it’s all because, at each stage of the delivery pipeline, the system “worked on my machine,” whether a developer’s laptop, a shared test environment configured differently from production, or some other unreliable environment.

    Solution: The solution is to configure every environment throughout the delivery pipeline as close to production as possible. The following are general guidelines that you may need to modify depending on local circumstances.

    If you have a staging environment, rather than twin production environments, it should be configured with all internal interfaces live and external interfaces stubbed, mocked, or virtualized. Even if this is as far as you take the idea, it will probably eliminate the need for release parties. But if you can, it’s good to continue upstream in the pipeline, to reduce unexpected delays in promoting code along.

    Test environments between development and staging should be running the same version of the OS and libraries as production. They should be isolated at the appropriate boundary based on the scope of testing to be performed.

    At the beginning of the pipeline, if it’s possible, develop on the same OS and same general configuration as production. It’s likely you will not have as much memory or as many processors as in the production environment. The development environment also will not have any live interfaces; all dependencies external to the application will be faked.

    At a minimum, match the OS and release level to production as closely as you can. For instance, if you’ll be deploying to Windows Server 2016, then use a Windows Server 2016 VM to run your quick CI build and unit test suite. Windows Server 2016 is based on NT 10, so do your development work on Windows 10 because it’s also based on NT 10. Similarly, if the production environment is Windows Server 2008 R2 (based on NT 6.1) then develop on Windows 7 (also based on NT 6.1). You won’t be able to eliminate every single configuration difference, but you will be able to avoid the majority of incompatibilities.

    Follow the same rule of thumb for Linux targets and development systems. For instance, if you will deploy to RHEL 7.3 (kernel version 3.10.x), then run unit tests on the same OS if possible. Otherwise, look for (or build) a version of CentOS based on the same kernel version as your production RHEL (don’t assume). At a minimum, run unit tests on a Linux distro based on the same kernel version as the target production instance. Do your development on CentOS or a Fedora-based distro to minimize inconsistencies with RHEL.

    If you’re using a dynamic infrastructure management approach that includes building OS instances from source, then this problem becomes much easier to control. You can build your development, test, and production environments from the same sources, assuring version consistency throughout the delivery pipeline. But the reality is that very few organizations are managing infrastructure in this way as of 2017. It’s more likely that you’ll configure and provision OS instances based on a published ISO, and then install packages from a private or public repo. You’ll have to pay close attention to versions.

    If you’re doing development work on your own laptop or desktop, and you’re using a cross-platform language (Ruby, Python, Java, etc.), you might think it doesn’t matter which OS you use. You might have a nice development stack on Windows or OSX (or whatever) that you’re comfortable with. Even so, it’s a good idea to spin up a local VM running an OS that’s closer to the production environment, just to avoid unexpected surprises.

    For embedded development where the development processor is different from the target processor, include a compile step in your low-level TDD cycle with the compiler options set for the target platform. This can expose errors that don’t occur when you compile for the development platform. Sometimes the same version of the same library will exhibit different behaviors when executed on different processors.

    Another suggestion for embedded development is to constrain your development environment to have the same memory limits and other resource constraints as the target platform. You can catch certain types of errors early by doing this.

    For some of the older back end platforms, it’s possible to do development and unit testing off-platform for convenience. Fairly early in the delivery pipeline, you’ll want to upload your source to an environment on the target platform and build and test there.

    For instance, for a C++ application on, say, HP NonStop, it’s convenient to do TDD on whatever local environment you like (assuming that’s feasible for the type of application), using any compiler and a unit testing framework like CppUnit.

    Similarly, it’s convenient to do COBOL development and unit testing on a Linux instance using GnuCOBOL; much faster and easier than using OEDIT on-platform for fine-grained TDD.

    However, in these cases, the target execution environment is very different from the development environment. You’ll want to exercise the code on-platform early in the delivery pipeline to eliminate works-on-my-machine surprises.

    Summary

    The works-on-my-machine problem is one of the leading causes of developer stress and lost time. The main cause of the works-on-my-machine problem is differences in configuration across development, test, and production environments.

    The basic advice is to avoid configuration differences to the extent possible. Take pains to ensure all environments are as similar to production as is practical. Pay attention to OS kernel versions, library versions, API versions, compiler versions, and the versions of any home-grown utilities and libraries. When differences can’t be avoided, then make note of them and treat them as risks. Wrap them in test cases to provide early warning of any issues.

    The second suggestion is to automate as much testing as possible at different levels of abstraction, merge code frequently, build the application frequently, run the automated test suites frequently, deploy frequently, and (where feasible) build the execution environment frequently. This will help you detect problems early, while the most recent changes are still fresh in your mind, and while the issues are still minor.

    Let’s fix the world so that the next generation of software developers doesn’t understand the phrase, “Works on my machine.”



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