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000-111 IBM Distributed Systems Storage Solutions Version 7

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000-111 exam Dumps Source : IBM Distributed Systems Storage Solutions Version 7

Test Code : 000-111
Test Name : IBM Distributed Systems Storage Solutions Version 7
Vendor Name : IBM
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IBM IBM Distributed Systems Storage

power techniques: using greater revenue Than at the start idea | killexams.com Real Questions and Pass4sure dumps

February 25, 2019 Timothy Prickett Morgan

Any model takes refinement, no matter if it is some thing a human spreadsheet jockey places collectively or it's a disbursed neural community it really is knowledgeable with laptop discovering recommendations to do some type of identification and manipulation of information. So it is with the power techniques profits model I put collectively a month ago in the wake of IBM reporting its monetary effects for the fourth quarter.

I didn't in fact suggest to get into it at the time. i was just going to collect a short table of the steady forex growth costs of the energy programs company and i simply kept going lower back in time and wondering what this data in fact supposed. steady forex increase prices are entertaining for month-to-month and 12 months-to-12 months comparisons for a company that does business in lots of currencies around the globe, nevertheless it doesn’t basically tell you the measurement of the energy programs company. As a refresher, here is what that boom chart for energy systems seems like:

So I went lower back in time and took my top-quality stab, according to assistance from the analysts at Gartner and IDC, on reckoning what the quarterly revenues for vigor techniques were in 2009, and i converted the consistent forex boom costs that IBM supplies each quarter with the as-said figures, which might be mentioned in varied currencies and converted to U.S. bucks at the conclusion of every quarter in accordance with the relative (and infrequently fluctuating) values of these currencies in opposition t the U.S. greenback.

I made what turned into an attractive first rate mannequin from this. however after getting some feedback and additionally giving it slightly more concept, I came to the conclusion that the preliminary revenue mannequin became a little brief on the external sales – which means folks that are reported as external income through IBM when it's talking to the Securities and exchange commission – in a couple of distinct and significant ways, some of which are less complicated to guesstimate than others.

the primary means it changed into shy is barely that it became simply too low for the exterior sales. no longer a whole lot, however a big volume that requires the model to be adjusted for 2018 and backcast the entire means returned to 2009. My initial mannequin reckoned that external vigour systems sales (again, meaning those now not bought to other IBM divisions however these bought to conclusion clients and channel companions) in 2018 came to a tad bit more than $1.6 billion, however I reckon now that it is greater like $1.78 billion. That may also no longer sound like a good deal, however it is an eleven p.c difference in the model, and i pride myself on being inside 5 percent or less in most issues. but this is very tough to do within the absence of information, and all i can say is that I believe it is more correct now in line with remarks and new statistics.

however that isn't all the vigor programs earnings that IBM does, and the image is more advanced, and this week I want to are trying to tackle some of that complexity to existing a more accurate photograph. apart from these external revenue of power programs gear to channel partners and users, IBM also “sells” energy methods machinery to the Storage techniques unit that is a component of systems group as the foundation of a considerable number of storage arrays, just like the DS8800 series disk/flash hybrid arrays, and software-defined storage like Spectrum Scale (GPFS) and Lustre parallel file methods as well as a number of object, key/price, and block storage engines. lower back in the day, IBM used to provide tips about how a great deal of its as-mentioned revenues came from servers, storage, and chip manufacturing, however it not does this. It does talk about increase in storage hardware, so that you can move forward from the historic facts to the brand new and take a look at to determine how an awful lot vigour programs iron, and its cost, is underpinning quite a lot of IBM storage. it is complicated to assert with any precision, but the power systems element of storage looks to be somewhere north of $200 million in 2018 – my bet is $226 million, up 15 p.c from 2017 degrees and considerably higher nonetheless than stages in 2016. In any experience, if you add that storage a part of the vigour methods enterprise in – which IBM doesn't escape itself – then the energy methods division likely brought in whatever north of $2 billion in revenues in 2018.

here is what the chart showing exterior energy equipment servers and internal storage-related power programs revenues appear to be collectively:

these storage-related vigour systems earnings are like icing on the cake, as you could see, ranging somewhere between eight % and 13 p.c of total vigour techniques earnings (with simply these two gadgets, which is not the comprehensive picture).

here's what this facts feels like if you annualize it and consolidate these power systems earnings:

That offers you a stronger thought of the slope of the earnings bars. And in case you like true statistics, right here is the table of the information at the back of that:

in case you wish to definitely comprehensive the photo on vigour programs hardware earnings, there's a different factor that must be added in: Strategic outsourcing contracts involving energy techniques machinery. There are some very significant agencies that have very gigantic compute complexes in line with power iron, and in a lot of situations, they are a good deal larger aggregations of programs than even gadget z retail outlets have. and a lot of of those valued clientele have IBM control these programs below an outsourcing contract during the world technology services enterprise. And when GTS buys iron to improve power equipment for shoppers, here is no longer protected within the externally said figures. it is hard to determine how an awful lot power equipment GTS consumes, and at what fee, but right here’s what we are able to say. IBM could make that rate anything it desired, any quarter that it desired, so there are doubtless practices in location to examine that apparatus that GTS buys at a good market value to keep away from the appearance of impropriety. in case you seem at the annual revenues for systems neighborhood, which comprises energy techniques and gadget z servers, operating techniques for these machines, and storage, IBM bought a complete of $eight.85 billion in hardware and operating systems, with $814 million of that being to interior IBM organizations; I reckon that most of that went to GTS for outsourcing, and further that about half went for servers, 1 / 4 went for storage, and a quarter for operating programs. It is not difficult to imagine that a couple of hundred million greenbacks in vigour systems iron turned into “bought” by way of GTS for outsourcing contracts last year. So perhaps the “true” revenues for vigour systems hardware is greater like $2.3 billion, and with might be a quarter of the $1.sixty two billion in operating programs being on vigour iron (the other three quarters comes from very high priced utility on gadget z mainframes), the breakdown of the $2.sixty six billion or so in energy programs earnings may seem like this:

this is a larger enterprise than many could have anticipated, and it is ecocnomic and growing. It can be worse. And it has been. And it is getting more suitable.

linked studies

Taking At Stab At Modeling The power methods business

power methods hold transforming into To finish Off 2018

programs A vivid Spot In mixed consequences For IBM

The Frustration Of now not figuring out How we're Doing

vigor programs Posts growth in the First Quarter

IBM’s systems community On The financial Rebound

large Blue gains, Poised For The Power9

The vigor Neine Conundrum

IBM Commits To Power9 improvements For huge vigour programs stores


Storage and AI work collectively in IBM’s multicloud approach | killexams.com Real Questions and Pass4sure dumps

an incredible focal point of the announcements from IBM Corp.’s feel convention final week worried artificial intelligence and making it attainable across all cloud platforms. This “AI far and wide” strategy applies to IBM’s storage method as smartly.

In December, IBM announced a storage equipment co-designed with Nvidia Corp. for AI workloads and a considerable number of facts equipment, equivalent to TensorFlow. AI reference structure is additionally integrated in IBM’s vigor line of servers.

there's curiously a further fundamental AI integration within the works, as IBM continues to center of attention on the hybrid cloud. “We’re working on a third one at the moment with a further main server dealer as a result of we desire our storage to be any place there’s AI and any place there’s a cloud — big, medium or small,” said Eric Herzog (pictured), chief advertising officer and vp of international storage channels at IBM.

Herzog spoke with John Furrier (@furrier) and Stu Miniman (@stu), co-hosts of theCUBE, SiliconANGLE Media’s cellular livestreaming studio, all over the IBM think event in San Francisco. They discussed IBM’s focal point on cyber resilience in its storage items and meeting customer wants in a multicloud ambiance. (* Disclosure below.)

New facets for resiliency

apart from multicloud and AI, IBM’s storage operation has additionally been focused on cyber resilience. In August, the business launched Cyber Incident recovery among the points included within the newest unlock of its Resiliency Orchestration platform.

the brand new product was designed to abruptly recuperate information and functions following a cyberattack. “sure, every person is used to the ‘super wall of China’ preserving you, and then of route chasing the unhealthy guy down when they breach you,” Herzog spoke of. “but once they breach you, it would certain be exceptional if every thing had records at leisure encryption.”

Enhancements to IBM’s storage portfolio over the past 12 months had been designed to accommodate client environments which are increasingly multicloud-oriented. The center of attention has been on software-defined storage solutions that stream and protect counsel in a wide range of compute ecosystems, as Herzog wrote in a recent weblog submit.

“You may also have NTT Cloud in Japan, you might also have Alibaba in China, you may also have IBM Cloud Australia, and then you may have Amazon in Latin the usa,” said Herzog, who seemed on the convention wearing a symbolic Hawaiian surfer shirt. “You don’t battle the wave; you experience the wave. And that’s what all and sundry is coping with.”

Watch the comprehensive video interview under, and be sure to take a look at more of SiliconANGLE’s and theCUBE’s insurance of the IBM feel experience. (* Disclosure: IBM Corp. backed this phase of theCUBE. Neither IBM nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

picture: SiliconANGLE considering you’re right here …

… We’d like to tell you about our mission and the way you could support us fulfill it. SiliconANGLE Media Inc.’s business model is based on the intrinsic value of the content, now not advertising. in contrast to many on-line publications, we don’t have a paywall or run banner promoting, because we wish to preserve our journalism open, devoid of affect or the need to chase traffic.The journalism, reporting and commentary on SiliconANGLE — together with are living, unscripted video from our Silicon Valley studio and globe-trotting video groups at theCUBE — take lots of complicated work, time and funds. conserving the high-quality excessive requires the assist of sponsors who are aligned with our imaginative and prescient of ad-free journalism content.

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IBM Mashes Up PowerAI And Watson laptop discovering Stacks | killexams.com Real Questions and Pass4sure dumps

previous in this decade, when the hyperscalers and the teachers that run with them were constructing laptop researching frameworks to transpose all types of statistics from one format to yet another – speech to text, text to speech, image to textual content, video to text, etc – they had been doing so now not only for scientific curiosity. They have been trying to resolve actual company problems and addressing the wants of consumers the usage of their application.

on the same time, IBM became trying to clear up a special difficulty, naming developing a question-answer system that could anthropomorphize the search engine. This effort became referred to as venture Blue J internal of IBM (not to be puzzled with the open source BlueJ built-in development atmosphere for Java), turned into wrapped up right into a utility stack known as DeepQA by means of IBM. It was this DeepQA stack, which changed into in keeping with the open source Hadoop unstructured facts storage and analytics engine that came out of Yahoo and yet another challenge referred to as Apache UIMA, which predates Hadoop through a number of years and which changed into designed by using IBM database specialists within the early 2000s to technique unstructured records like textual content, audio, and video. This Deep QA stack turned into embedded within the Watson QA device that changed into designed to play Jeopardy towards people, which we noted in detail here eight years ago. The Apache UIMA stack became the key a part of the WatsonQA gadget that did natural language processing that parsed out the speech in a Jeopardy reply, transformed it to text, and fed it into the statistical algorithms to create the Jeopardy question.

Watson gained the competition towards human Jeopardy champs Brad Rutter and Ken Jennings, and a brand – which invoked IBM founder Thomas Watson and his admonition to “think” in addition to medical professional Watson, the sidekick of fictional supersleuth Sherlock Holmes – became born.

rather than make Watson a product on the market, IBM offered it as a provider, and pumped the QA device full of records to take on the healthcare, economic services, energy, advertising and media, and education industries. This turned into, most likely, a mistake, however on the time, within the wake of the Jeopardy championship, it felt like every thing was moving to the cloud and that the SaaS mannequin became the appropriate manner to go. IBM in no way truly talked in amazing aspect about how DeepQA become constructed, and it has in a similar way no longer been particular about how this Watson stack has modified over time – eight years is a very long time within the laptop gaining knowledge of space.  It is not clear if Watson is material to IBM’s revenues, but what is obvious is that desktop researching is strategic for its methods, utility, and services organizations.

So it truly is why IBM is at last bringing together all of its desktop getting to know tools and inserting them beneath the Watson brand and, very importantly, making the Watson stack attainable for purchase so it can also be run on private datacenters and in different public clouds anyway the one that IBM runs. To be actual, the Watson capabilities as well because the PowerAI machine gaining knowledge of practising frameworks and adjunct tools tuned up to run on clusters of IBM’s vigour systems machines, are being brought collectively, and they'll be put into Kubernetes containers and distributed to run on the IBM Cloud private Kubernetes stack, which is accessible on X86 systems as well as IBM’s own energy iron, in virtualized or naked metallic modes. It is that this encapsulation of this new and comprehensive Watson stack with IBM Cloud private stack that makes it portable throughout inner most datacenters and other clouds.

by the way, as a part of the mashup of those tools, the PowerAI stack that makes a speciality of deep getting to know, GPU-accelerated machine gaining knowledge of, and scaling and disbursed computing for AI, is being made a core part of the Watson Studio and Watson computing device getting to know (Watson ML) utility equipment. This integrated utility suite gives commercial enterprise facts scientists an end-to-conclusion developer tools. Watson Studio is an built-in development atmosphere according to Jupyter notebooks and R Studio. Watson ML is a collection of machine and deep studying libraries and mannequin and records administration. Watson OpenScale is AI mannequin monitoring and bias and equity detection. The software previously known as PowerAI and PowerAI enterprise will continue to be developed by the Cognitive techniques division. The Watson division, in case you don't seem to be usual with IBM’s organizational chart, is part of its Cognitive solutions community, which comprises databases, analytics equipment, transaction processing middleware, and numerous functions allotted both on premises or as a provider on the IBM Cloud.

it's unclear how this Watson stack could trade in the wake of IBM closing the pink Hat acquisition, which should still occur earlier than the conclusion of the 12 months. nonetheless it is competitively priced to expect that IBM will tune up all of this software to run on pink Hat commercial enterprise Linux and its own KVM digital machines and OpenShift implementation of Kubernetes and then push definitely complicated.

it is likely beneficial to evaluate what PowerAI is all about after which display how it is being melded into the Watson stack. earlier than the combination and the identify changes (extra on that in a second), here is what the PowerAI stack looked like:

in accordance with Bob Picciano, senior vp of Cognitive systems at IBM, there are more than 600 business clients that have deployed PowerAI tools to run machine researching frameworks on its energy programs iron, and clearly GPU-accelerated systems like the power AC922 equipment that's on the heart of the “Summit” supercomputer at o.k.Ridge national Laboratory and the sibling “Sierra” supercomputer at Lawrence Livermore countrywide Laboratory are the main IBM machines individuals are using to do AI work. here's a good looking good birth for a nascent industry and a platform that is relatively new to the AI crowd, but most likely not so diverse for commercial enterprise shoppers which have used vigor iron in their database and software tiers for decades.

The preliminary PowerAI code from two years ago started with models of the TensorFlow, Caffe, PyTorch, and Chainer laptop getting to know frameworks that massive Blue tuned up for its energy processors. The large innovation with PowerAI is what's known as colossal model help, which makes use of the coherency between Nvidia “Pascal” and “Volta” Tesla GPU accelerators and Power8 and Power9 processors in the IBM vigour methods servers – enabled via NVLink ports on the energy processors and tweaks to the Linux kernel – to allow tons greater neural network practicing fashions to be loaded into the system. all of the PowerAI code is open source and dispensed as code or binaries, and so far only on power processors. (We suspect IBM will go agnostic on this eventually, considering the fact that Watson tools should run on the large public clouds, which with the exception now of the IBM Cloud, won't have energy methods accessible. (Nimbix, a professional in HPC and AI and a smaller public cloud, does present energy iron and helps PowerAI, by the way.)

underneath this, IBM has created a groundwork referred to as PowerAI business, and here's no longer open source and it is simply obtainable as part of a subscription. PowerAI enterprise adds Message Passing Interface (MPI) extensions to the laptop getting to know frameworks – what IBM calls disbursed Deep getting to know – as well as cluster virtualization and computerized hyper-parameter optimization options, embedded in its Spectrum Conductor for Spark (sure, that Spark, the in-memory processing framework) tool. IBM has also added what it calls the Deep getting to know influence module, which includes equipment for managing records (such as ETL extraction and visualization of datasets) and managing neural community fashions, together with wizards that imply the way to most advantageous use records and models. On correct of this stack, IBM’s first industrial AI software that it's selling is referred to as PowerAI vision, which may also be used to label graphic and video statistics for practicing fashions and instantly instruct models (or augment present models supplied with the license).

So in spite of everything of the alterations, here's what the brand new Watson stack feels like:

As you could see, the Watson desktop researching stack helps a lot more desktop discovering frameworks, above all the SnapML framework that got here out of IBM’s analysis lab in Zurich this is providing a major efficiency capabilities on vigor iron compared to working frameworks like Google’s TensorFlow. here's surely a greater complete stack for computer discovering, including Watson Studio for developing fashions, the important Watson computer getting to know stack for practicing and deploying fashions in creation inference, and now Watson OpenScale (it's mislabeled within the chart) to computer screen and help enrich the accuracy of fashions based on how they are running in the box as they infer issues.

For the moment, there is not any trade in PowerAI business licenses and pricing all over the first quarter, however after that PowerAI commercial enterprise may be introduced into the Watson stack to add the distributed GPU laptop studying working towards and inference capabilities atop power iron to that stack. So Watson, which began out on Power7 machines taking part in Jeopardy, is coming again domestic to Power9 with production machine discovering functions in the enterprise. We aren't definite if IBM will present an identical allotted machine discovering capabilities on non-vigor machines, nevertheless it appears possible that is valued clientele wish to run the Watson stack on premises or in a public cloud, it will ought to. vigor techniques will must stand on its own merits if that comes to move, and given the merits that Power9 chips have with reference to compute, I/O and memory bandwidth, and coherent reminiscence throughout CPUs and GPUs, that may additionally not be as a great deal of an impact as we might suppose. The X86 architecture will must win by itself deserves, too.


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HPC in Life Sciences Part 1: CPU Choices, Rise of Data Lakes, Networking Challenges, and More | killexams.com real questions and Pass4sure dumps

For the past few years HPCwire and leaders of BioTeam, a research computing consultancy specializing in life sciences, have convened to examine the state of HPC (and now AI) use in life sciences.

Without HPC writ large, modern life sciences research would quickly grind to a halt. It’s true most life sciences research computing is less focused on tightly-coupled, low-latency processing (traditional HPC) and more dependent on data analytics and managing (and sieving) massive datasets. But there is plenty of both types of compute and disentangling the two has become increasingly difficult. Sophisticated storage schemes have long been de rigueur and recently fast networking has become important (no surprise given lab instruments’ prodigious output). Lastly, striding into this shifting environment is AI – deep learning and machine learning – whose deafening hype is only exceeded by its transformative potential.

Ari Berman, BioTeam

This year’s discussion included Ari Berman, vice president and general manager of consulting services, Chris Dagdigian, one of BioTeam’s founders and senior director of infrastructure, and Aaron Gardner, director of technology. Including Dagdigian, who focuses largely on the enterprise, widened the scope of insights so there’s a nice blend of ideas presented about biotech and pharma as well as traditional academic and government HPC.

Because so much material was reviewed we are again dividing coverage into two articles. Part One, presented here, examines core infrastructure issues around processor choices, heterogeneous architecture, network bottlenecks (and solutions), and storage technology. Part Two, scheduled for next week, tackles the AI’s trajectory in life sciences and the increasing use of cloud computing in life sciences. In terms of the latter, you may be familiar with NIH’s STRIDES (Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability) program which seeks to cut costs and ease cloud access for biomedical researchers.

Enjoy

HPCwire: Let’s tackle the core compute. Last year we touched potential rise of processor diversity (AMD, Intel, Arm, Power9) and certainly AMD seems to have come on strong. What’s your take on changes in core computing landscape?

Chris Dagdigian: I can be quick and dirty. My view in the commercial and pharmaceutical and biotech space is that, aside from things like GPUs and specialized computing devices, there’s not a lot of movement away from the mainstream processor platforms. These are people moving in 3-to-5-year purchasing cycles. These are people who standardized on Intel after a few years of pain during the AMD/Intel wars and it would take something of huge significance to make them shift again. In commercial biopharmaceutical and biotech there’s not a lot of interesting stuff going on in the CPU set.

The only other thing that’s interesting that’s happening is as more and more of this stuff goes to the cloud or gets virtualized, a lot of the CPU stuff actually gets hidden from the user. So there’s a growing part of my community (biomedical researchers in enterprise) where the users don’t even know what CPU their code is running on. That’s particularly true for things like AWS batch, and AWS Lambda (serverless computing services) and that sort of stuff running in the cloud. I think I’ll stop here are say on the commercial side they are slow and conservative and it’s still an Intel world and the cloud is hiding a lot of the true CPU stuff particularly as people go serverless.

Aaron Gardner: That’s an interesting point. As more clouds have adopted the Epyc CPU, some people may not realize they are running on them when they start instances. I would say also that the rise of informatics as a service and workflows as a service is going to abstract things even more. It’s relatively easy today to run most code with some level of optimization across the Intel and AMD CPUs. But the gap widens a bit when you talk about, is the code, or portions of it being GPU accelerated, or did you switch architectures from AMD64 to Power9 or something like that.

We talked last year about a transition from compute clusters being a hub fed by large-spoke data systems towards a data cluster where the hub is the data lake with its various moving pieces and storage tiers, but the spokes are all the different types of heterogeneous compute services that span and support the workload run on that system. We definitely have seen movement towards that model. If you look at all Cray’s announcements in the last few months, everything from what they are doing with Shasta and Slingshot, and work towards making the CS (cluster supercomputers) and XC (tightly coupled supercomputers) work seamlessly, interoperably, in the same infrastructure, we’re seeing companies like Cray and others gearing up for a heterogeneous future where they are going to support multiple processor architectures and optimize for multiple processor architectures as well as accelerators, CPUs and GPUs, and have it all work together in a coherent whole. That’s actually very exciting, because it’s not about betting on one particular horse or another; it’s about how well you are going to integrate across architectures, both traditional and non-traditional.

Ari Berman: Circling back to what Chris said. Life sciences historically has been sort of slow to jump in and adopt new stuff just to try it or to see if it will be three percent faster because the differences gained in knowledge generation at this point in life science for those three percent are not ground breaking – it’s fine to wait a little while. Those days, however, are dwindling because of the amount of data being generated and the urgency with which it has to be processed and also the backlog of data that has to be processed.

So we are not in life sciences at a point where – other than the differentiation of GPUs – applications are being designed specifically for different system processors other than for Intel. There’s some caveats to that. Normally as long as you can compile it and run it on one of the main system processors and it can run on a normal version of Linux, they are not optimizing for that; the exceptions to that are some of the built in math libraries that can be taken advantage of on the Intel system platform, some of the data offloading for moving data to and from CPUs from remote or even internally, memory bandwidth really matters a lot, and some of those things are differentiated based on what kind of research you are doing.

HPCwire: It sounds a little like the battle for mindshare and market share among processor vendors doesn’t matter as much in life sciences, at least at the user level. Is that fair?

Ari Berman: Well, we really like a lot of the future architectures. AMD is coming out with for better memory bandwidth to handle things like PCIe links, having new interconnects between CPUs, and also the connection to the motherboard. One of the big bottlenecks Intel still has to solve is how do you get data to and from the machine from external sources. Internally they have optimized the bandwidth a whole lot, but if you have huge central sources of data from parallel file systems, you still have to get it in and out of that system, and there are bottlenecks there.

Aaron Gardner: With the Rome architecture moving forward, AMD has provided a much better approach to memory access, moving away from NUMA (nonuniform memory) to a central memory controller with uniform latency across dies. This is really important when you have up to 64 cores per socket. Moving back towards a more favorable memory access model on a per node design level I think is really going to help provide advantages to workloads in the life sciences and that is certainly something we are looking at testing and exploring over the next year.

Ari Berman: I do think that for the first time in a while Power9 has some potential relevance, mostly because Summit and Sierra (IBM-based supercomputers) coming into play and those machines being built on Power9. I think people are exploring it but I don’t know that it will make much of a play outside of just pure HPC. The other thing I meant to bring up is a place where I think AMD is ahead of Intel in fab technology. AMD is already manufacturing at 7nm versus the 14nm. I thought that it was really innovative of AMD to do a multiple nanometer fabrication for their next release of processors where the IO core is 14nm and the processing core is 7nm because, just for power and distribution efficiency.

Aaron Gardner: In terms of market share, I think AMD has been extremely strategic over the last 18 months because when you look at places that got burned by AMD in the past when it exited the server market, there were not enough benefits to warrant jumping back in fully right away. But AMD is really geared towards the economies-of-scale type plays such as in the cloud where any advantage in efficiency is going to be appreciated. So I think they have been strategic [in choosing target markets] and we’ll see over the next couple of years how it plays out. I think we are at the moment not in a place where the client needs to specify a certain processor. We are going to see the integrators influence here, what they choose to put together in their heterogeneous HPC systems portfolio, influence what CPUs people get and that may really effect the winners and losers over time.

ARM we see continue to grow but not explosively and I’d say Power is certainly interesting. Having the large Power systems at the top of the TOP500 has really validated Power9 for use in capability supercomputing. How those are used though versus the GPUs for target workloads is interesting. In general we may be headed to a future where the CPU is used to turn on the GPU for certain workloads. Nvidia would probably favor that model. It’s just very interesting the interplay between CPU and GPU; it really does have to do with whether you are accelerating a small number of codes to the nth degree or you are trying to have more diverse application support which is where multiple CPU and GPU architectures are going to be needed.

Ari Berman: Using GPUs is still a huge thing for lots of different reasons. At the moment GPUs are hyped for AI and ML, but they have been used extensively for a lot of the simulation space, Schrodinger suite, molecular modeling, quantum chemistry, those sorts of things, and also down into phylogenetic inference, special inheritance, things like that. There are many great applications for graphic processors, but really I would agree with others that it really boils down to system processors and GPUs at the moment in life sciences. I did hear anecdotally from a couple of folks in the industry that were using the IBM Q cloud just to try quantum [computing], just to see how it worked with really high level genomic alignment and they kind of got it to work and I’ll leave it at that.

HPCwire: We probably don’t devote enough coverage to networking given its importance driven by huge datasets and the rise of edge computing. What’s the state of networking in life sciences?

Chris Dagdigian: In pharmaceuticals and biotech, Ethernet rules the world. The high speed low latency interconnects are still in niche environments. When we do see non-ethernet fabrics in the commercial world they are being used for parallel filesystems or in specialized HPC chemistry & molecular modeling application environments where MPI message passing latency actually matters. However I will bluntly say networking speed is now the most critical issue in my HPC world. I feel that compute and storage at petascale are largely tractable problems. Moving data at scale within an organization or outside the boundaries of your firewall to a collaborator or a cloud is the single biggest rate limiting bottleneck for HPC in pharma and biotech. Combine with that the cost high speed Ethernet has not gone down as fast as the cost of commoditization in storage and compute. So we are in this double whammy world where we desperately need fast networks.

The corporate networking people are fairly smug about the 10 gig and 40 gig links they have in the datacenter core whereas we need 100 gig networking going outside the datacenter, 100 gig going outside the building, sometimes we need 100 gig links to a particular lab. Honestly the way that I handle this in enterprise is I am helping research organizations become a champion for the networking groups; they traditionally are under budgeted and don’t typically have 40 gig and 100 gig and 400 gig on their radar because you know they are looking at bandwidth graphs for their edge switches or their firewalls and they just don’t see the insane data movement that we have to do between the laboratory instrument and a storage system. The second thing, and I have utterly failed at it, is articulating that there are products other than Cisco in the world. That argument does not fly in enterprise because there is a tremendous installed base. So I am in the catch 22 of I pay a lot of money for Cisco 40 gig and 100 gig and I just have to live with it.

Ari Berman: I would agree networking is one of the major challenges. Depending on what granularity you are looking at, I think most of the HPCwire readers will care a lot about interconnects on clusters. Starting there, I would say we are seeing a fairly even distribution of pure Ethernet on the back end because of vendors like Arista for instance, which is producing more affordable 100 gig low latency Ethernet that can be put on the back end so you don’t have to do the whole RDMA versus TCP/IP dance necessarily. But most clusters are still using InfiniBand on their back end.

In life sciences I would say that we still see Mellanox predominantly as the back end. I have not seen life-science-directed organizations [use] a whole lot of Omni-Path (OPA). I have seen it at the NSF supercomputer centers, used to great effect, and they like it a lot, but not really so much in life sciences. I’d say the speed and diversity and the abilities of the Mellanox implementation could really outclass what is available in OPA today. I think the delays in OPA2 have hurt them. I do think the new interconnects like Shasta/Slingshot from Cray are paving the way to producing a reasonable competitor to where Mellanox is today.

Moving out from that, Chris is right. There are so many people using the cloud that don’t upgrade their internet connections to a wide enough bandwidth or take their security enough out of the way or optimize it enough so that people can effectively use the cloud for data-intensive applications, that getting the data there is impossible. You can use the cloud but only if the data is already there. That’s a huge problem.

Internally, a lot of organizations have moved to hot spots of 100 gig to be able to move data effectively between datacenters and from external data sources but a lot of 10 gig still predominates. I’d say that there is a lot of 25 gig implementations and 50 gig implementations now. 40 gig sort went by the wayside. That’s because of the 100 gig optical carriers where they are actually made up of four individual wavelinks and so what they did was to just break those out and so the form factors have shrunk.

Going back to the cluster back end. In life sciences the reason high performance networking on the back end of a cluster is really important isn’t necessarily for inter-process communications, it’s for storage delivery to nodes. Almost every implementation has a large parallel distributed file system where all of the data are coming from at one point or another. You have to get them to the CPU and that backend network needs to be optimized for that traffic.

Aaron Gardner: That’s a common case in the life sciences. We primarily look at storage performance to bring data to nodes and even to move between nodes versus message passing for parallel applications. That’s starting to shift a little bit but that’s traditionally been how it is. We usually have looked at a single high performance fabric talking to a parallel files system. Whereas HPC as a whole has for a long time dealt with having a fast fabric for internode communications for large scale parallel jobs and then having a storage fabric that was either brought to all of the nodes or somehow shunted into the other fabric using IO router nodes.

“One of the things that is very interesting with Cray announcing Slingshot is the ability to speak both an internal low latency HPC optimized protocol as well as Ethernet, which in the case of HPC storage removes the need for IO router nodes, instead allowing the HCA (host channel adapters) and switching to handle the load and protocol translation and all of that. Depending on how transparent and easy it is to implement Slingshot at the small and mid-scale I think that is a potential threat to the continued prevalence of traditional InfiniBand in HPC, which is essentially Mellanox today.”

HPCwire: We’ve talked for a number of years about the revolution in life sciences instruments, and how the gush of data pouring from them overwhelms research IT systems. That has put stress on storage and data management. What’s you sense of the storage challenge today?

Chris Dagdigian: My sense is storing vast amounts of data is not particularly challenging these days. There’s a lot of products on the market, very many vendors to choose from, and the actual act of storing the data is relatively straightforward. However, no one has centrally cracked the how we manage it, how do we understand what we’ve got on disk, how do we carefully curate and maintain that stuff. Overwhelmingly the dominant storage pattern in my world is if they are not using a parallel files system for speed it’s overwhelmingly scale-out network attached storage (NAS). But we are definitely in the era where some of the incumbent NAS vendors are starting to be seen as dinosaurs or being placed on a 3-year or 4-year upgrade cycle.

The other thing is there’s still a lot of interest in hybrid storage, storage that spans the cloud and can be replicated into the cloud. The technology is there but in many cases the pipes are not. So it is still relatively difficult to either synchronize or replicate and maintain a consistent storage namespace unless you are a really solid organization with really fast pipes to the outside world. We still see the problems of lots of islands of storage. The only other thing I will say is I am known for saying the future of scientific data at rest belongs in an object store, but that it’s going to take a long time to get there because we have so many dependencies on things that expect to see files and folders. I have customers that are buying petabytes of network attached storage but at the same time they are also buying petabytes of object storage. In some cases they are using the object storage natively; in other cases the object storage is their data continuity or backup target.

In terms of file system preference, the commercial world is not only conservative but also incredibly concerned with admin burden and value so almost universally it is going to be a mainstream choice like GPFSsupported by DDN or IBM. There are lots of really interesting alternatives like BeeGFS but the issue really is the enterprise is nervous about fancy new technologies, not because of the fancy new technologies but because they have to bring new people in to do the care and feeding.

Aaron Gardner: Some of the challenges with how we see storage deployed across life science organizations is how close to the bottom have they been driven. With traditional supercomputing, you’re trying to get the fastest storage you can, and the most of it, for the least amount of money. The support needed is not the primary driver. In HPC as a whole, Lustre and GPFS/Spectrum Scale are still the predominate players in terms of parallel file system. The interesting stuff over the last year or so has been Lustre trading hands (from Intel to DDN). With DDN leading the charge, the ecosystem is still being kept open and I think carefully crafted so other vendors can provide solutions independently from DDN. We do see IBM stepping up Spectrum Scale performance and Spectrum Scale 5offering a lot of good features proven out and demonstrated on the Summit and Sierra type systems, making Spectrum Scale every bit as relevant as it ever was.

As far as performant parallel file systems there are interesting alternatives. There is more presence and momentum behind BeeGFS than we have seen in prior years. We see some adoption and clients interested in trying and adopting it but the number deployments in production and at a large scale is still pretty limited.

These days object storage is seen more like a tap that you turn on and you are getting your object storage through AWS or Azure or GCP. If you are buying it for on-premise, there’s little differentiation seen between object vendors. That’s the perception at least. We are seeing interest in what we call next generation storage systems and file systems – things like WekaIO that provide NVMe over fabrics (NVMeOF) on the front end and export their own NVMeOF native file system as opposed to block storage. This removes the need to use something like Spectrum Scale or Lustre to provide the file system and can drain cold data to object storage either on premise or in the cloud. We do see that as a viable model moving forward.

I would add say that speaking to NVME over fabrics in general; that it seems to be growing and becoming established as most of the new storage vendors coming on the scene are currently architecting that way. That’s good in our book. We certainly see performance advantages but it really matters how it’s done—it is important that the software stack driving the NVME media has been purpose built for NVME over fabrics or at least significantly redesigned. Something ground up like WekaIO or VAST will perform very well. On the other hand you could choose NVME over fabrics as the hardware topology for a storage system, but if you then layer on a legacy file system that hasn’t been updated for it you might not see much benefit.

Couple of other quick notes. It seems like storage benchmarking in HPC has been receiving more attention both in terms of measuring throughput and metadata operations, with the latter being valued and seen as one of the primary bottlenecks that govern the absolute utility of a cluster. For projects like the IO500 we’ve seen an uptick in participation, both from national labs as well as vendors and other organizations. The last thing worth mentioning is data management. Scraping data for ML training data sets, for example, is one of the things driving us to understand the data we store better than we have in the past. One of the simple ways to do that is to tag your data and we are seeing more files systems coming on the scene with a focus on tagging as a core in-built feature. So while they come at the problem from different angles you could look at what companies like Atavium is doing for primary storage or Igneous for secondary storage, providing the ability to tag data on ingest and the ability to move data (policy-driven) according to tags. This is something that we have talked about for a long time and have helped a lot of clients tackle.”

Link to Part Two (HPC in Life Sciences Part 2: Penetrating AI’s Hype and the Cloud’s Haze)


Asavie IoT Connect Service Now Available on AWS Marketplace to Expedite Enterprise IoT Projects | killexams.com real questions and Pass4sure dumps

Asavie, a leader in secure Enterprise Mobility and Internet of Things (IoT) Connectivity,announced today that Asavie IoT Connect is now available on Amazon Web Services (AWS) Marketplace. The on-demand secure, network connectivity service enables developers to deploy IoT projects in minutes. By combining the flexibility and reach of AWS with Asavie IoT Connect’s seamless edge-to-Cloud secure cellular network management, businesses can quickly deploy and scale their IoT projects in a trusted end-to-end environment.

Asavie IoT Connect is an on-demand, secure connectivity service designed to connect IoT edge devices to the AWS cloud. Developers can provision their IoT devices in minutes with a seamless and secure private cellular connectivity to transmit data to the Amazon Virtual Private Cloud (Amazon VPC). Asavie IoT Connect enables a completely private network, extending from edge IoT devices to AWS, that shields devices from public Internet borne cyberthreats such as malware and Distributed Denial of Service (DDoS) attacks.

The availability of such an on-demand seamless secure connection from the edge device to the cloud facilitates enterprise adoption of IoT by removing some of the complexity and skills required to manage the lifecycle of an IoT deployment. As observed by Emil Berthelsen, Snr. Director & Analyst with Gartner, “Moving deeper into IoT solutions and architectures, however, will require new skills around connectivity, integration, cloud and possibly analytics. On the one hand, connecting and integrating IoT endpoints, platforms and enterprise systems will be critical to ensure the secure flow of data from the edge to the platform. At another level, providing suitable processing and storage capabilities, and enabling the use of future cloud-based services, will require skills from the cloud service area.” [i ]

Garth Fort, Director, AWS Marketplace, Amazon Web Services, Inc. said, “IoT is top of mind for many of our customers in multiple sectors. We’re continuing to make it easier for customers to innovate and meet their growing IoT business needs and we’re delighted to welcome Asavie IoT Connect on AWS Marketplace to help customers quickly and securely deploy IoT solutions.”

Brendan Carroll, CEO with industrial IoT sensor manufacturer, EpiSensor said, “Our global customers rely on the calibre of our products to continually monitor and provide insights on their industrial processes, 24/7. In turn we rely on our suppliers Asavie and AWS to provide the resilient, secure connectivity and storage services to enable us to fulfill our exacting service level agreements across the globe.”

“The ease with which the Asavie IoT Connect service allows us seamlessly connect individual devices to the AWS cloud infrastructure allows us to scale device-based deployments anywhere in the world,” added Carroll.

Asavie CEO, Ralph Shaw said, “As an AWS IoT Competency Partner, Asavie has already demonstrated relevant technical proficiency and proven customer success, delivering solutions seamlessly on AWS. Today’s announcement builds on this foundation and expands our distribution capabilities to the enterprise market. With Asavie and AWS, enterprises can now confidently implement their IoT go to market strategies across multiple territories.”

“By simplifying the secure integration of data from edge IoT devices to the cloud, Asavie empowers global businesses to drive increased cost savings, reduce risk and expedite their IoT implementations,” continued Shaw.

Visit Asavieat MWC onbooth7F30.

About Asavie

Asavie makes secure connectivity simple for any size of mobility or IoT deployment in a hyper-connected world. Asavie’s on-demand services power the secure and intelligent distribution of data to connected devices anywhere. We enable enterprise customers globally to harness the power of the internet of things and mobile devices to transform and scale their businesses. Strategic distribution and technology partners include AT&T, AWS, Dell, IBM, Microsoft, Singtel, Telefonica, Verizon and Vodafone. Asavie is an ISO 27001 certified company. For more information visit: www.asavie.com and follow @Asavie on Twitter.

[i] Gartner: 2017 Strategic Roadmap for Successful Enterprise IoT Journeys - 29 November 2017 – Author Emil Berthelsen

View source version on businesswire.com: https://www.businesswire.com/news/home/20190224005118/en/

SOURCE: Asavie"> <Property FormalName="PrimaryTwitterHandle" Value="@Asavie

For AsavieHugh Carroll, Asavie, + 353 1 676 3585/+353 087 136 9869 hugh.carroll@asavie.comAnne Marie McCallion, ReturnPR +353 86 8349329 annemarie@returnpr.com

Copyright Business Wire 2019


Blockchain May Be Overkill for Most IIoT Security | killexams.com real questions and Pass4sure dumps

Blockchain crops up in many of the pitches for security software aimed at the industrial IoT. However, IIoT project owners, chipmakers and OEMs should stick with security options that address the low-level, device- and data-centered security of the IIoT itself, rather than the effort to promote blockchain as a security option as well as an audit tool.

Only about 6% of Industrial IoT (IIoT) project owners chose to build IIoT-specific security into their initial rollouts, while 44% said it would be too expensive, according to a 2018 survey commissioned by digital security provider Gemalto.

Currently, only 48% of IoT project owners can see their devices well enough to know if there has been a breach, according to the 2019 version of Gemalto’s annual survey.

Software packages that could fill in the gaps were few and far between. This is largely because securing devices aimed at industrial functions requires more memory, storage or update capability than typical IIoT/IoT devices currently have. That makes it difficult to apply security software to networks with IIoT hardware, according to Steve Hanna, senior principal at Infineon Technologies, who co-wrote an endpoint-security best-practices guide published by the Industrial Internet consortium in 2018.

Still, the recognition is widespread that security is a problem with connected devices. Spending on IIoT/IoT-specific security will grow 25.1% per year, from $1.7 billion during 2018, to $5.2 billion by 2023, according to a 2018 market analysis report from BCC Research. Another study, by Juniper Research, predicts 300% growth by 2023, to just over $6 billion.

Since 2017, a group of companies including Cisco, Bosch, Gemalto, IBM and others have promoted blockchain as a way to create a tamper-proof provenance for everything from chips to whole devices. By creating an auditable history, where each new event or change in status has to be verified by 51% of the members of the group participating in a particular ledger, it should be possible to trace an individual component from point of sale to the original manufacturer to verify whether it’s been tampered with.

Blockchain also can be used to track and verify sensor data, prevent duplication or the insertion of malicious data and provide ongoing verification of the identity of individual devices, according to an analysis from IBM, which promotes the use of blockchain in both technical and financial functions.

Use of blockchain in securing IIoT/IoT assets among those polled in Gemalto’s latest survey rose to 19%, up from 9% in 2017. And 23% of respondents said they believe blockchain is an ideal solution to secure IIoT/IoT assets.

Any security may be better than none, but some of the more popular options don’t translate well into actual IIoT-specific security, according to Michael Chen, design for security director at Mentor, a Siemens Business.

“You have to look at it carefully, know what you’re trying to accomplish and what the security level is,” Chen said. “Public blockchain is great for things like the stock exchange or buying a home, because on a public blockchain with 50,000 people if you wanted to cheat you’d have to get more than 50% to cooperate. Securing IIoT devices, even across a supply chain, is going to be a lot smaller group, which wouldn’t be much reassurance that something was accurate. And meanwhile, we’re still trying to figure out how to do root of trust and key management and a lot of other things that are a different and more of an immediate challenge.”

Others agree. “Using blockchain to track the current location and state of an IoT device is probably not a good use of the technology,” according to Michael Shebanow, vice president of R&D for Tensilica at Cadence. “Public ledgers are a means of securely recording information in a distributed manner. Unless there is a defined need to record location/state in that manner, then using blockchain is a very high-overhead means of doing so. In general, applications probably don’t need that level of authenticity check.”

Limitations of blockchainsEven the most robust public blockchain efforts are often less efficient than the solutions they replace. But more importantly, they don’t make a process more secure by removing the need for trust, argues security guru Bruce Schneier, CTO of IBM Resilient.

Blockchain reduces the amount of trust we have to put in humans and requires that we trust computers, networks and applications that may be single points of failure. By contrast, a human-driven legal system has many potential points of failure and recovery. One can make the other more efficient, but there’s no reason to assume that simply shifting trust to machines, regardless of context or quality of execution, will make anything better, Schneier wrote.

Public-ledger verification methods can be applied to many aspects of identity and supply chain for IIoT/IoT networks, according to a 2018 report from Boston Consulting Group. Only 25% of the applications BCG identified had completed the proof-of-concept phase, however, and problems such as faked or plagiarized approvals identified in cryptocurrency cases, a lack of standards, performance issues and regulatory uncertainty all raised doubts about its usefulness as a way to manage basic security and authentication this early in the maturity of both the IIoT and blockchain.

“When we have blockchain worked out for supply chain, we’ll probably have the means to apply it to chips and IoT, but it probably doesn’t work the other way,” Chen said.

The overhead required for blockchain verifications of location or status data for thousands of devices is off-putting, and it’s much easier to identify hardware using a public/private key—especially if the private key is secured by a number identified in a physically unclonable function, Shebanow agreed. “Barring a lab attack, PUF via hardware implementation makes it nearly impossible to spoof an ID, whereas software is never 100% secure. It is virtually impossible to prove that a complex software system has no back door.”

The bottom line: Stick with root of trust, secure boot and build from there, until there’s an efficient blockchain template for IoT.

Related StoriesBlockchain: Hype, Reality, OpportunitiesTechnology investments and rollouts are accelerating, but there is still plenty of room for innovation and improvement.IoT Device Security Makes Slow ProgressWhile attention is being paid to security in IoT devices, still more must be done.Are Devices Getting More Secure?Manufacturers are paying more attention to security, but it’s not clear whether that’s enough.Why The IIoT Is Not SecureDon’t blame the technology. This is a people problem.



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Cisco [318 Certification Exam(s) ]
Citrix [48 Certification Exam(s) ]
CIW [18 Certification Exam(s) ]
Cloudera [10 Certification Exam(s) ]
Cognos [19 Certification Exam(s) ]
College-Board [2 Certification Exam(s) ]
CompTIA [76 Certification Exam(s) ]
ComputerAssociates [6 Certification Exam(s) ]
Consultant [2 Certification Exam(s) ]
Counselor [4 Certification Exam(s) ]
CPP-Institue [2 Certification Exam(s) ]
CPP-Institute [1 Certification Exam(s) ]
CSP [1 Certification Exam(s) ]
CWNA [1 Certification Exam(s) ]
CWNP [13 Certification Exam(s) ]
Dassault [2 Certification Exam(s) ]
DELL [9 Certification Exam(s) ]
DMI [1 Certification Exam(s) ]
DRI [1 Certification Exam(s) ]
ECCouncil [21 Certification Exam(s) ]
ECDL [1 Certification Exam(s) ]
EMC [129 Certification Exam(s) ]
Enterasys [13 Certification Exam(s) ]
Ericsson [5 Certification Exam(s) ]
ESPA [1 Certification Exam(s) ]
Esri [2 Certification Exam(s) ]
ExamExpress [15 Certification Exam(s) ]
Exin [40 Certification Exam(s) ]
ExtremeNetworks [3 Certification Exam(s) ]
F5-Networks [20 Certification Exam(s) ]
FCTC [2 Certification Exam(s) ]
Filemaker [9 Certification Exam(s) ]
Financial [36 Certification Exam(s) ]
Food [4 Certification Exam(s) ]
Fortinet [13 Certification Exam(s) ]
Foundry [6 Certification Exam(s) ]
FSMTB [1 Certification Exam(s) ]
Fujitsu [2 Certification Exam(s) ]
GAQM [9 Certification Exam(s) ]
Genesys [4 Certification Exam(s) ]
GIAC [15 Certification Exam(s) ]
Google [4 Certification Exam(s) ]
GuidanceSoftware [2 Certification Exam(s) ]
H3C [1 Certification Exam(s) ]
HDI [9 Certification Exam(s) ]
Healthcare [3 Certification Exam(s) ]
HIPAA [2 Certification Exam(s) ]
Hitachi [30 Certification Exam(s) ]
Hortonworks [4 Certification Exam(s) ]
Hospitality [2 Certification Exam(s) ]
HP [750 Certification Exam(s) ]
HR [4 Certification Exam(s) ]
HRCI [1 Certification Exam(s) ]
Huawei [21 Certification Exam(s) ]
Hyperion [10 Certification Exam(s) ]
IAAP [1 Certification Exam(s) ]
IAHCSMM [1 Certification Exam(s) ]
IBM [1532 Certification Exam(s) ]
IBQH [1 Certification Exam(s) ]
ICAI [1 Certification Exam(s) ]
ICDL [6 Certification Exam(s) ]
IEEE [1 Certification Exam(s) ]
IELTS [1 Certification Exam(s) ]
IFPUG [1 Certification Exam(s) ]
IIA [3 Certification Exam(s) ]
IIBA [2 Certification Exam(s) ]
IISFA [1 Certification Exam(s) ]
Intel [2 Certification Exam(s) ]
IQN [1 Certification Exam(s) ]
IRS [1 Certification Exam(s) ]
ISA [1 Certification Exam(s) ]
ISACA [4 Certification Exam(s) ]
ISC2 [6 Certification Exam(s) ]
ISEB [24 Certification Exam(s) ]
Isilon [4 Certification Exam(s) ]
ISM [6 Certification Exam(s) ]
iSQI [7 Certification Exam(s) ]
ITEC [1 Certification Exam(s) ]
Juniper [64 Certification Exam(s) ]
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Lotus [66 Certification Exam(s) ]
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LSI [3 Certification Exam(s) ]
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McData [3 Certification Exam(s) ]
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Microsoft [374 Certification Exam(s) ]
Mile2 [3 Certification Exam(s) ]
Military [1 Certification Exam(s) ]
Misc [1 Certification Exam(s) ]
Motorola [7 Certification Exam(s) ]
mySQL [4 Certification Exam(s) ]
NBSTSA [1 Certification Exam(s) ]
NCEES [2 Certification Exam(s) ]
NCIDQ [1 Certification Exam(s) ]
NCLEX [2 Certification Exam(s) ]
Network-General [12 Certification Exam(s) ]
NetworkAppliance [39 Certification Exam(s) ]
NI [1 Certification Exam(s) ]
NIELIT [1 Certification Exam(s) ]
Nokia [6 Certification Exam(s) ]
Nortel [130 Certification Exam(s) ]
Novell [37 Certification Exam(s) ]
OMG [10 Certification Exam(s) ]
Oracle [279 Certification Exam(s) ]
P&C [2 Certification Exam(s) ]
Palo-Alto [4 Certification Exam(s) ]
PARCC [1 Certification Exam(s) ]
PayPal [1 Certification Exam(s) ]
Pegasystems [12 Certification Exam(s) ]
PEOPLECERT [4 Certification Exam(s) ]
PMI [15 Certification Exam(s) ]
Polycom [2 Certification Exam(s) ]
PostgreSQL-CE [1 Certification Exam(s) ]
Prince2 [6 Certification Exam(s) ]
PRMIA [1 Certification Exam(s) ]
PsychCorp [1 Certification Exam(s) ]
PTCB [2 Certification Exam(s) ]
QAI [1 Certification Exam(s) ]
QlikView [1 Certification Exam(s) ]
Quality-Assurance [7 Certification Exam(s) ]
RACC [1 Certification Exam(s) ]
Real-Estate [1 Certification Exam(s) ]
RedHat [8 Certification Exam(s) ]
RES [5 Certification Exam(s) ]
Riverbed [8 Certification Exam(s) ]
RSA [15 Certification Exam(s) ]
Sair [8 Certification Exam(s) ]
Salesforce [5 Certification Exam(s) ]
SANS [1 Certification Exam(s) ]
SAP [98 Certification Exam(s) ]
SASInstitute [15 Certification Exam(s) ]
SAT [1 Certification Exam(s) ]
SCO [10 Certification Exam(s) ]
SCP [6 Certification Exam(s) ]
SDI [3 Certification Exam(s) ]
See-Beyond [1 Certification Exam(s) ]
Siemens [1 Certification Exam(s) ]
Snia [7 Certification Exam(s) ]
SOA [15 Certification Exam(s) ]
Social-Work-Board [4 Certification Exam(s) ]
SpringSource [1 Certification Exam(s) ]
SUN [63 Certification Exam(s) ]
SUSE [1 Certification Exam(s) ]
Sybase [17 Certification Exam(s) ]
Symantec [134 Certification Exam(s) ]
Teacher-Certification [4 Certification Exam(s) ]
The-Open-Group [8 Certification Exam(s) ]
TIA [3 Certification Exam(s) ]
Tibco [18 Certification Exam(s) ]
Trainers [3 Certification Exam(s) ]
Trend [1 Certification Exam(s) ]
TruSecure [1 Certification Exam(s) ]
USMLE [1 Certification Exam(s) ]
VCE [6 Certification Exam(s) ]
Veeam [2 Certification Exam(s) ]
Veritas [33 Certification Exam(s) ]
Vmware [58 Certification Exam(s) ]
Wonderlic [2 Certification Exam(s) ]
Worldatwork [2 Certification Exam(s) ]
XML-Master [3 Certification Exam(s) ]
Zend [6 Certification Exam(s) ]





References :


Dropmark : http://killexams.dropmark.com/367904/11587765
Wordpress : http://wp.me/p7SJ6L-Vv
Issu : https://issuu.com/trutrainers/docs/000-111
Dropmark-Text : http://killexams.dropmark.com/367904/12129069
Blogspot : http://killexamsbraindump.blogspot.com/2017/11/review-000-111-real-question-and.html
RSS Feed : http://feeds.feedburner.com/Pass4sure000-111RealQuestionBank
weSRCH : https://www.wesrch.com/business/prpdfBU1HWO000CHAC
Calameo : http://en.calameo.com/books/004923526d29f762a374d
publitas.com : https://view.publitas.com/trutrainers-inc/pass4sure-000-111-practice-tests-with-real-questions
Box.net : https://app.box.com/s/y8s0ia8x4a2sjctkyluk9f3zxq0es81w
zoho.com : https://docs.zoho.com/file/5ptno29d914de95544b28810055264631e8ab






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