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: 30 existent Questions

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IBM IBM Optimization Technical Mastery

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There comes a degree where respectable ideas don't seem to breathe satisfactory; conceivable, proven solutions must rear flight.

here's the region where cloud computing finds itself these days, certainly when the dialog turns towards unreasonable efficiency computing (HPC).

The ample majority of businesses at the present time are leveraging the agility, can impregnate benefits, and comfort offered by artery of the public cloud services mannequin. HPC amenities are additionally exploring the benefits of cloud, but most likely no longer for the very factors. Whereas more traditional corporations are most often looking to public cloud functions providers for backup and catastrophe recuperation options or the alternative to reduce storage prices by parking seldom-accessed records sets on much less expensive cloud storage, HPC amenities usually minister to breathe exploring the chances of accommodating spikes admired with the aid of dynamically adding and subtracting cloud-primarily based substances.

These entire sound relish very superb concepts, but making them work, with no solicitude and reliably, has been a existent problem. Cloud compute materials, in contrast to cloud storage, will likewise breathe extra costly than on-premises infrastructure. security is always a consideration. moving colossal HPC records units over networks can likewise breathe prohibitively gradual and cumbersome. and you requisite to beget requisite to beget purposes which can operate acceptably on cloud-primarily based supplies.

When off-web page cloud resources are integrated with on-premises computing, the outcomes is referred to as a hybrid cloud architecture. There is not any one preempt technique to assemble an HPC hybrid cloud computing cluster. each web site is distinct, and users elope distinctive workloads that beget very different aid consumption patterns. as an instance, some abysmal discovering practicing consumes big amounts of information to provide a little mannequin. Others are especially CPU intensive, or GPU intensive. The hybrid cluster solution truly carried out will of direction breathe pushed by means of the workloads HPC users want to run, however two hybrid architectures often upward push to the desirable when IT architects tackle the challenges of constructing cloud flit for HPC – the “stretch cluster” configuration and the “multi-cluster” configuration. To accommodate spikes widespread, companies working natural HPC environments are increasingly looking at hybrid clouds to supply the additional means when it’s really needed.  Stretch and multi-cluster hybrid cloud solutions can champion address this problem by using enabling dynamic entry to cloud resources.

A stretch cluster skill that you just lengthen or augment your on-premises HPC compute cluster with further cloud-based mostly compute nodes, when vital. This architecture assumes that you beget a compute cluster in a different vicinity – either on-premises or configured in a cloud. This structure is formally described as a unique cluster stretched over a wide enviornment network (WAN) in order that compute nodes on the other zone or within the cloud communicate with a master scheduling host on the originating location. notwithstanding a superb deal easier in theory than a multi-cluster, this skill that each one communique and coordination with the master scheduler occurs over the WAN, which can likewise breathe a source of added can impregnate or decreased reliability.

A multi-cluster hybrid cloud configuration is a greater complicated structure, since it provides a grasp scheduler working in the cloud. by means of together with this additional master scheduler within the cloud, the architecture eliminates much of the communication from cloud compute nodes to the on-premises master. the two grasp schedulers as a substitute trade project meta-facts in a “job forwarding” mannequin. in this mannequin, clients on-premises post application workloads to a queue on-premises, which in whirl forwards that workload to the cloud for execution. Upon job completion, the scheduler running in the cloud synchronizes the job popularity with the on-premises environment and a notification is equipped to the user.

The query turns into, what are the deployment, configuration, and optimization particulars for HPC stretch and multi-cluster hybrid cloud solutions? How conclude I truly merit this issue fly? A simultaneous announcement from IBM provides the solutions. In a crucial step along the pathway to possible, respectable, easy-to-use hybrid cloud options, IBM has developed and launched a deployment ebook that allows using IBM Spectrum LSF with Amazon net services (AWS). This LSF cloud deployment e book builds on an current relationship with AWS. IBM is featuring potential, features, and management capabilities in an endeavor to give IBM Spectrum LSF clients quickly, bendy access to AWS offerings.

the unique deployment book offers information for structure a wide array of customizable stretch and multi-cloud IBM Spectrum LSF cluster configurations that permit clients to extra without problems and conveniently gain the agility, can impregnate benefits, and convenience provided through cloud computing.

IBM Spectrum LSF is Part of a comprehensive Suite of solutions supporting traditional HPC and unreasonable throughput environments, in addition to ample facts, synthetic Intelligence, GPU, laptop learning, and containerized workloads, amongst many others. IBM Spectrum LSF, the core of the Suite, is a powerful workload administration platform for worrying, disbursed HPC environments. It gives an entire set of intelligent, policy-driven scheduling points that champion maximize utilization of compute infrastructure elements while optimizing software performance. LSF is the HPC workload management ordinary, with probably the most finished set of capabilities – from license scheduling and session scheduling to superior analytics.

IBM Spectrum LSF Suite comes in three variants and includes a few extra components corresponding to IBM Spectrum LSF useful resource Connector, which permits coverage-pushed cloud bursting to entire fundamental cloud functions, together with IBM Cloud, AWS, Google, and Azure. And now, IBM is providing variable consume licensing for IBM Spectrum LSF which capacity that LSF users should breathe in a position to elope the solution essentially any place, and pay best for what they use.

The cloud offers many merits, but constructing efficacious hybrid cloud solutions isn’t so essential – previously. With the main-edge performance offered with the aid of IBM Spectrum LSF and the assist of the brand unique LSF deployment e book for AWS, HPC facilities are a gross lot greater able to merit cloud flit for them.

study leading-side HPC and AI options on the IBM sales space next week at SC18 (booth #3433) in Dallas, Texas. Register here for technical briefings and consumer group classes.

read greater about dynamic hybrid cloud with IBM Spectrum LSF at the IBM IT Infrastructure weblog – IBM Spectrum LSF Goes Multicloud.

a more in-depth appear on the deployment e book and assisting substances for constructing IBM Spectrum LSF hybrid-cloud configurations with Amazon internet functions can likewise breathe discovered here.




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P2050-007 exam Dumps Source : IBM Optimization Technical Mastery Test v1

Test Code : P2050-007
Test appellation : IBM Optimization Technical Mastery Test v1
Vendor appellation : IBM
: 30 existent Questions

Unbelieveable overall performance of P2050-007 exam bank and examine manual.
All in all, killexams.com was a superb artery for me to prepare for this exam. I passed, but was a little disappointed that now entire questions on the exam were 100% the very as what killexams.com gave me. Over 70% were the very and the repose was very similar - Im not confident if this is a superb thing. I managed to pass, so I contemplate this counts as a superb result. But hold in understanding that even with killexams.com you still requisite to learn and consume your brain.


in which will I determine cloth for P2050-007 examination?
My planning for the examination P2050-007 was incorrect and subjects seemed tough for me as nicely. As a snappy reference, I depended on the Q/A by artery of killexams.com and it conveyed what I wished. tons favor to the killexams.com for the assistance. To the point noting system of this aide became not tough to seize for me as nicely. I surely retained entire that I should. A score of 92% became agreeable, contrasting with my 1-week war.


Do you want state-of-the-art dumps of P2050-007 exam to pellucid the examination?
P2050-007 questions from killexams.com are top notch, and mirror exactly what rear a glance at hub offers you at the P2050-007 examination. I cherished everything approximately the killexams.com guidance cloth. I surpassed with over 80%.


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This is my first time that I took this service. I feel very confident in P2050-007 but. I prepare my P2050-007 using questions and answers with exam simulator softare by killexams.com team.


Tips & tricks to certify P2050-007 exam with high scores.
hi! i am julia from spain. want to skip the P2050-007 exam. but. My English is very negative. The language is simple and contours are brief . No solicitude in mugging. It helped me wrap up the training in 3 weeks and that i passed wilh 88% marks. now not capable of crack the books. long lines and hard words merit me sleepy. wished an smooth manual badly and ultimately located one with the killexams.com brain dumps. I were given entire query and solution . extraordinary, killexams! You made my day.


revel in assured via making ready P2050-007 dumps.
some precise men cant deliver an alteration to the worlds manner but they could only let you know whether or notyouve got been the simplest man who knew the artery to conclude that and that i want to breathe acknowledged on this global and merit my personal result and i beget been so lame my complete manner but I understand now that I desired to gain a bypassin my P2050-007 and this can merit me illustrious perhaps and yes i am short of glory however passing my A+ exams with killexams.com was my morning and night time glory.


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I am joyful to inform that I beget successfully passed the P2050-007 Exam. In this context I must admit that your Question Bank did alleviate (if not fully) to tied over the exam as the questions asked in the exam were not fully covered by your question bank. HOwever I must congratulate your endeavor to merit us technically sound with your s. Thanks to killexams.com for clearing my P2050-007 exam in first class.


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I might propose this query monetary institution as a should ought to each person whos getting prepared for the P2050-007 examination. It modified into very advantageous in getting an conviction as to what contour of questions were coming and which regions to consciousness. The exercise check supplied changed into additionally outstanding in getting a sense of what to anticipate on examination day. As for the solutions keys supplied, it emerge as of wonderful assist in recollecting what I had learnt and the explanationssupplied beget been smooth to understand and definately brought rate to my conviction on the priority.


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Unfriendly Skies: Predicting Flight Cancellations Using Weather Data, Part 2 | killexams.com existent questions and Pass4sure dumps

Ricardo Balduino and Tim Bohn

Early Flight, Creative Commons Introduction

As they described in Part 1 of this series, their objective is to alleviate prognosticate the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. They consume historical flights data and historical weather data to merit predictions for upcoming flights.

Over the course of this four-part series, they consume different platforms to alleviate us with those predictions. Here in Part 2, they consume the IBM SPSS Modeler and APIs from The Weather Company.

Tools used in this consume case solution

IBM SPSS Modeler is designed to alleviate determine patterns and trends in structured and unstructured data with an intuitive visual interface supported by advanced analytics. It provides a ambit of advanced algorithms and analysis techniques, including text analytics, entity analytics, decision management and optimization to deliver insights in near real-time. For this consume case, they used SPSS Modeler 18.1 to create a visual representation of the solution, or in SPSS terms, a stream. That’s right — not one line of code was written in the making of this blog.

We likewise used The Weather Company APIs to retrieve historical weather data for the ten airports over the year 2016. IBM SPSS Modeler supports calling the weather APIs from within a stream. That is accomplished by adding extensions to SPSS, available in the IBM SPSS Predictive Analytics resources page, a.k.a. Extensions Hub.

A proposed solution

In this blog, they propose one workable solution for this problem. It’s not meant to breathe the only or the best workable solution, or a production-level solution for that matter, but the discussion presented here covers the typical iterative process (described in the sections below) that helps us accumulate insights and refine the predictive model across iterations. They inspirit the readers to try and Come up with different solutions, and provide us with your feedback for future blogs.

Business and data understanding

The first step of the iterative process includes understanding and gathering the data needed to train and test their model later.

Flights data — We gathered 2016 flights data from the US Bureau of Transportation Statistics website. The website allows us to export one month at a time, so they ended up with 12 csv (comma separated value) files. They used IBM SPSS Modeler to merge entire the csv files into one set and to select the ten airports in their scope. Some data clean-up and formatting was done to validate dates and hours for each flight, as seen in design 1.

Figure 1 — gathering and preparing flights data in IBM SPSS Modeler

Weather data — From the Extensions Hub, they added the TWCHistoricalGridded extension to SPSS Modeler, which made the extension available as a node in the tool. That node took a csv file listing the 10 airports latitude and longitude coordinates as input, and generated the historical hourly data for the entire year of 2016, for each airport location, as seen in design 2.

Figure 2 — gathering and preparing weather data in IBM SPSS Modeler

Combined flights and weather data — To each flight in the first data set, they added two unique columns: origin and DEST, containing the respective airport codes. Next, flight data and the weather data were merged together. Note: the “stars” or SPSS super nodes in design 3 are placeholders for the diagrams in Figures 1 and 2 above.

Figure 3 — combining flights and weather data in IBM SPSS Modeler Data preparation, modeling, and evaluation

We iteratively performed the following steps until the desired model qualities were reached:

· Prepare data

· fulfill modeling

· Evaluate the model

· Repeat

Figure 4 shows the first and second iterations of their process in IBM SPSS Modeler.

Figure 4 — iterations: prepare data, elope models, evaluate — and conclude it again First iteration

To start preparing the data, they used the combined flights and weather data from the previous step and performed some data cleanup (e.g. took keeping of null values). In order to better train the model later on, they filtered out rows where flight cancellations were not related to weather conditions (e.g. cancellations due to technical issues, security issues, etc.)

Figure 5 — imbalanced data found in their input data set

This is an Interesting consume case, and often a hard one to solve, due to the imbalanced data it presents, as seen in design 5. By “imbalanced” they express that there were far more non-cancelled flights in the historical data than cancelled ones. They will dispute how they dealt with imbalanced data in the following iteration.

Next, they defined which features were required as inputs to the model (such as flight date, hour, day of the week, origin and destination airport codes, and weather conditions), and which one was the target to breathe generated by the model (i.e. prognosticate the cancellation status). They then partitioned the data into training and testing sets, using an 85/15 ratio.

The partitioned data was fed into an SPSS node called Auto Classifier. This node allowed us to elope multiple models at once and preview their outputs, such as the zone under the ROC curve, as seen in design 6.

Figure 6 — models output provided by the Auto Classifier node

That was a useful step in making an initial selection of a model for further refinement during subsequent iterations. They decided to consume the Random Trees model since the initial analysis showed it has the best zone under the curve as compared to the other models in the list.

Second iteration

During the second iteration, they addressed the skewedness of the original data. For that purpose, they chose one of the SPSS nodes called SMOTE (Synthetic Minority Over-sampling Technique). This node provides an advanced over-sampling algorithm that deals with imbalanced datasets, which helped their selected model travail more effectively.

Figure 7 — distribution of cancelled and non-cancelled flights after using SMOTE

In design 7, they notice a more balanced distribution between cancelled and non-cancelled flights after running the data through SMOTE.

As mentioned earlier, they picked the Random Trees model for this sample solution. This SPSS node provides a model for tree-based classification and prediction that is built on Classification and Regression Tree methodology. Due to its characteristics, this model is much less prostrate to overfitting, which gives a higher likelihood of repeating the very test results when you consume unique data, that is, data that was not Part of the original training and testing data sets. Another odds of this method — in particular for their consume case — is its faculty to manipulate imbalanced data.

Since in this consume case they are dealing with classification analysis, they used two common ways to evaluate the performance of the model: confusion matrix and ROC curve. One of the outputs of running the Random Trees model in SPSS is the confusion matrix seen in design 8. The table shows the precision achieved by the model during training.

Figure 8 — Confusion Matrix for cancelled vs. non-cancelled flights

In this case, the model’s precision was about 95% for predicting cancelled flights (true positives), and about 94% for predicting non-cancelled flights (true negatives). That means, the model was rectify most of the time, but likewise made wrong predictions about 4–5% of the time (false negatives and False positives).

That was the precision given by the model using the training data set. This is likewise represented by the ROC curve on the left side of design 9. They can see, however, that the zone under the curve for the training data set was better than the zone under the curve for the testing data set (right side of design 9), which means that during testing, the model did not fulfill as well as during training (i.e. it presented a higher rate of errors, or higher rate of False negatives and False positives).

Figure 9 — ROC curves for the training and testing data sets

Nevertheless, they decided that the results were still superb for the purposes of their discussion in this blog, and they stopped their iterations here. They inspirit readers to further refine this model or even to consume other models that could decipher this consume case.

Deploying the model

Finally, they deployed the model as a repose API that developers can convoke from their applications. For that, they created a “deployment branch” in the SPSS stream. Then, they used the IBM Watson Machine Learning service available on IBM Bluemix here. They imported the SPSS stream into the Bluemix service, which generated a scoring endpoint (or URL) that application developers can call. Developers can likewise convoke The Weather Company APIs directly from their application code to retrieve the forecast data for the next day, week, and so on, in order to pass the required data to the scoring endpoint and merit the prediction.

A typical scoring endpoint provided by the Watson Machine Learning service would glance relish the URL shown below.

https://ibm-watson-ml.mybluemix.net/pm/v1/score/flights-cancellation?accesskey=<provided by WML service>

By passing the expected JSON cadaver that includes the required inputs for scoring (such as the future flight data and forecast weather data), the scoring endpoint above returns if a given flight is likely to breathe cancelled or not. This is seen in design 10, which shows a convoke being made to the scoring endpoint — and its response — using an HTTP requester tool available in a web browser.

Figure 10 — actual request URL, JSON body, and response from scoring endpoint

Notice in the JSON response above that the deployed model predicted this particular flight from Newark to Chicago would breathe 88.8% likely to breathe cancelled, based on forecast weather conditions.

Conclusion

IBM SPSS Modeler is a powerful tool that helped us visually create a solution for this consume case without writing a unique line of code. They were able to supervene an iterative process that helped us understand and prepare the data, then model and evaluate the solution, to finally deploy the model as an API for consumption by application developers.

Resources

The IBM SPSS stream and data used as the basis for this blog are available on GitHub. There you can likewise find instructions on how to download IBM SPSS Modeler, gain a key for The Weather Channel APIs, and much more.


The Week In Review: Design | killexams.com existent questions and Pass4sure dumps

M&AIoT-focused remembrance chipmaker Adesto Technologies acquired S3 Semiconductors, a provider of mixed-signal and RF ASICs and IP. Based in Ireland, S3 Semiconductors was founded in 1986. S3 Semiconductors will become a industry unit of Adesto and will continue to operate under its current model in the $35 million deal. S3 Semiconductor’s parent company, S3 Group, will continue as a part company focused on smart medical devices.

ToolsCadence debuted a unique design-for-reliability tool for analog and mixed-signal IC design. The Legato Reliability Solution incorporates analog defect analysis and simulation to evaluate the faculty to eradicate die with manufacturing defects and resulting test escapes that understanding sphere failures, as well as electro-thermal and aging analysis to avert thermal overstress and prognosticate wear-out.

Aldec made several updates to its Riviera-PRO verification platform, adding a UVM Register Generator for automated register model creation. The UVM Register Generator supports input of IP-XACT component files and CSV files. Other additions include a Unit Linting office that runs ALINT-PRO in the background and champion for the latest extensions to VHDL.

XJTAG uncorked free DFT software for users of Mentor’s Xpedition product to augment the design for test and debug capabilities of the schematic capture and PCB design environment. XJTAG DFT helper helps validate rectify JTAG chain connectivity by finding and repairing errors.

IPArasan Chip Systems uncorked its MIPI I3C Host Controller Interface (I3C HCI) Master IP core compliant to the recently released MIPI I3C HCI Specification 1.0. The IP kit includes a Linux software stack compatible with the I3C HCI Master IP Core, an I3C Hardware evolution Kit to enable prototyping and compliance testing of I3C products, and the I3C Slave IP Core.

Chips&Media launched its first computer vision IP, which implements a convolutional neural network capable of processing 4K resolution at 60 frames per second input in real-time for demur detection. The IP targets edge devices for automotive and surveillance applications.

Chipus taped out a set of its ultra-low power analog IP on SilTerra’s 0.18um I18L IoT Platform. The test chip includes a temperature sensor, RTC oscillators, 6MHz oscillators, and an ultra-low power bandgap reference.

StandardsThe Si2 Compact Model Coalition published the Open Model Interface, a C-language application programming interface that supports SPICE compact model extensions. OMI allows for simulation and analysis of physical effects such as self-heating and aging as well as design optimization. It is based on TMI2, the TSMC Model Interface, which was donated to Si2 by TSMC in 2014. So far, OMI supports BSIM, BSIM-CMG, HiSIM2, and BSIM-SOI.

The MIPI Alliance released MIPI Touch, a family of publicly available specifications for the design and implementation of palpate applications, such as in smartphones. The endeavor ties together several MIPI specifications for touch, sensor, and software integration: MIPI palpate Command Set (MIPI TCS) for writing device drivers across operating systems; MIPI palpate Adaptation Layer for I3C (MIPI ALI3C) to translate palpate commands for consume on the MIPI I3C protocol; MIPI I3C Host Controller Interface (MIPI I3C HCI), which enables palpate commands and multiple data streams for advanced systems; and MIPI I3C v1.0, which connects an application processor to a palpate sensor.

DealsRambus and GigaDevice formed a joint venture, Reliance Memory, in China to focus on evolution and commercialization of Resistive Ram (RRAM), a non-volatile remembrance that works by changing the resistance of materials when different voltages are applied. The joint venture has investment funding from THG Ventures, West apex Capital, Walden International, and Zhisland Capital.

Esperanto Technologies inked deals for both NetSpeed Systems’ network-on-chip IP and design environment and UltraSoC’s embedded analytics and debug IP for its Supercomputer-on-a-Chip for AI applications. The 7nm chip will integrate thousands of RISC-V compatible processors each with vector/tensor accelerators.

NetSpeed Systems and Northwest Logic are teaming up on a high throughput remembrance subsystem solution that uses NetSpeed’s interconnect IP and Northwest Logic’s HBM2 and GDDR6 remembrance controllers for customers designing SoCs for hyperscale and automotive applications. The companies will provide a common platform to extend NetSpeed’s dynamic-priority altitude approach to the remembrance controller so that system-level traffic-flow priorities are handled in an end-to-end solution.

NumbersIntel Capital placed investments in two firms: Movellus and SiFive. Movellus provides generator tools enabling automatic creation and implementation of PLL, DLL, and LDO digital IP and will consume the funds to accelerate market deployment and adoption of its technology. SiFive is a provider of RISC-V IP cores, SoCs, and evolution boards, as well a RISC-V-focused IP catalog.

Rambus released monetary results for first quarter 2018 with revenue of $46.4 million. On a GAAP basis, there was a loss per share of $0.36; on a non-GAAP basis there was a loss per share of $0.10. Rambus changed to a unique reporting yardstick this year; under the previous reporting standard, revenue for Q1 2018 was $100.5 million, up 4% from the very quarter last year. GAAP income per share for the quarter was $0.05, up 66.7% from $0.03 in Q1 2017; non-GAAP income per share was $0.21, up 23.5% from $0.17. The company posted record revenue for its remembrance and interface IP cores.

Jesse Allen   (all posts)Jesse Allen is the scholarship hub administrator and a senior editor at Semiconductor Engineering.

Cisco refused to participate in NSS Labs report on SD-WAN | killexams.com existent questions and Pass4sure dumps

Cisco refused to activate the Viptela software-defined WAN product NSS Labs bought for testing, leaving the research...

firm with a noticeable pocket in its recent comparative report on SD-WAN vendors.

Cisco did not provide a understanding for refusing to activate the product NSS Labs had purchased for between $30,000 and $40,000, NSS Labs CEO Vikram Phatak said this week. "There was no understanding given other than, effectively, they didn't want to breathe tested (for the NSS Labs report)."

Cisco's action marked the first time a vendor had refused to whirl on a product NSS Labs had bought for evaluation, Phatak said. Cisco's Viptela team had initially told NSS Labs it would champion the test, which led the solid to buy the product.

"That's a first for us, candidly," Phatak said. "And given Cisco's ethical rules and so on -- rules of conduct -- I'm in shock because normally, they're pretty straightforward to travail with."

Cisco's response

After initially refusing to dispute the matter, Cisco released a statement that said NSS Labs's proposed testing methodology "fell well short" of reflecting "the enterprise-grade nature of customer SD-WAN requirements."

"We shared existent customer deployment requirements with them, but their feedback wasn't incorporated," Cisco said. "At that point, they decided not to participate in the test."

Not participating, however, usually doesn't express the vendor will avert the test from going forward. Typically, when a company refuses to associate a test, NSS Labs will conduct it alone.

"If someone says they don't want to breathe tested, they say, 'That's great, but if a product is superb enough to breathe sold to the public, it's superb enough to breathe tested,'" Phatak said. "We're going to buy it, and we'll report to the public."

NSS Labs wants a refund

NSS Labs wants Cisco to refund the money spent on Viptela. It is hoping it can gain the money back without going to court.

"I hope it doesn't Come to that," Phatak said. "We haven't talked to any lawyers. I'm assuming that we'll breathe able to beget the conversation and gain their money back."

Typically, NSS Labs buys products, and the vendors whirl them on relish they would for any other customer.

That's a first for us, candidly. And given Cisco's ethical rules and so on -- rules of conduct -- I'm in shock. Vikram PhatakCEO, NSS Labs

NSS Labs illustrious Cisco's refusal to activate the Viptela purchase in its SD-WAN Comparative Report, which was the company's first SD-WAN test. Not having Cisco in the evaluation left out one of the largest SD-WAN vendors and a major tech company.

In the first quarter, London-based IHS Markit listed Cisco as No. 4 in the SD-WAN market, just behind Silver Peak. VMware was first with a 19% share, followed by Aryaka with 18%.

The NSS Labs report, released this month, compared the products of nine vendors, including VMware's NSX SD-WAN, formerly VeloCloud. VMware is Cisco's largest competitor.

NSS Labs had likewise planned to include Silver Peak in the comparison but illustrious it was unable to obtain the product in time for testing.

Tech companies often cite recommended ratings in NSS Labs reports in marketing materials. In April, Cisco highlighted in a blog post the organization's "recommended" rating for the Cisco Advanced Malware Protection for Endpoints product.

Based on its recent SD-WAN tests, NSS Labs recommended products from VMware, Talari Networks and Fortinet and listed products from Citrix Systems, FatPipe Networks, Forcepoint and Versa Networks as "verified." Tech buyers should account recommended and verified products as candidates for purchase, according to NSS Labs.

The company issued "caution" ratings for Barracuda Networks and Cradlepoint, which means companies should not deploy their products without a comprehensive evaluation, NSS Labs said.



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