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There comes a point where respectable concepts are not ample; potential, proven options must bewitch flight.

here's the location the position cloud computing finds itself today, notably when the dialog turns towards extreme performance computing (HPC).

The big majority of agencies at the present time are leveraging the agility, can permeate benefits, and convenience offered by means of the general public cloud services mannequin. HPC amenities are too exploring the advantages of cloud, however most likely no longer for the very motives. Whereas extra tolerable corporations are most commonly trying to public cloud services providers for backup and catastrophe restoration solutions or the election to reduce storage prices by using parking seldom-accessed data sets on less expensive cloud storage, HPC amenities are more likely to live exploring the percentages of accommodating spikes widespread via dynamically including and subtracting cloud-based components.

These entire sound enjoy very first rate concepts, but making them work, effectively and reliably, has been a true problem. Cloud compute components, unlike cloud storage, may too live extra expensive than on-premises infrastructure. security is at entire times a consideration. relocating colossal HPC statistics units over networks may too live prohibitively gradual and cumbersome. and you deserve to fill deserve to fill purposes that can function acceptably on cloud-based mostly substances.

When off-website cloud supplies are integrated with on-premises computing, the outcomes is referred to as a hybrid cloud architecture. There isn't any one amend way to construct an HPC hybrid cloud computing cluster. each website is diverse, and clients hasten different workloads that fill very diverse useful resource consumption patterns. for example, some abysmal gaining erudition of training consumes huge quantities of facts to supply a small mannequin. Others are incredibly CPU intensive, or GPU intensive. The hybrid cluster reply actually applied will of course live pushed through the workloads HPC clients exigency to run, however two hybrid architectures commonly upward thrust to the commandeer when IT architects wield the challenges of creating cloud soar for HPC – the “stretch cluster” configuration and the “multi-cluster” configuration. To accommodate spikes favourite, companies working common HPC environments are more and more hybrid clouds to deliver the further means when it’s in reality essential.  Stretch and multi-cluster hybrid cloud options can support tackle this problem by using enabling dynamic access to cloud supplies.

A stretch cluster means that you just prolong or augment your on-premises HPC compute cluster with extra cloud-primarily based compute nodes, when vital. This architecture assumes that you fill a compute cluster in yet another location – both on-premises or configured in a cloud. This architecture is formally defined as a single cluster stretched over a wide area network (WAN) in order that compute nodes on the different location or in the cloud talk with a master scheduling host at the originating place. even though a powerful deal less difficult in view than a multi-cluster, this capacity that every one communication and coordination with the grasp scheduler happens over the WAN, which can too live a source of further cost or lowered reliability.

A multi-cluster hybrid cloud configuration is a greater advanced architecture, since it provides a master scheduler working within the cloud. by way of together with this additional grasp scheduler within the cloud, the architecture eliminates a lot of the conversation from cloud compute nodes to the on-premises master. the two grasp schedulers in its position exchange assignment meta-facts in a “job forwarding” mannequin. in this mannequin, users on-premises submit software workloads to a queue on-premises, which in turn forwards that workload to the cloud for execution. Upon job completion, the scheduler operating within the cloud synchronizes the job popularity with the on-premises atmosphere and a notification is provided to the user.

The query turns into, what are the deployment, configuration, and optimization details for HPC stretch and multi-cluster hybrid cloud options? How attain I definitely manufacture this component fly? A recent announcement from IBM provides the answers. In a crucial step alongside the pathway to viable, authentic, handy-to-use hybrid cloud solutions, IBM has developed and launched a deployment ebook that facilitates the exercise of IBM Spectrum LSF with Amazon internet services (AWS). This LSF cloud deployment steer builds on an existing relationship with AWS. IBM is proposing abilities, functions, and administration capabilities in an trouble to supply IBM Spectrum LSF users quickly, springy entry to AWS offerings.

the brand unique deployment e-book provides suggestions for constructing a huge purview of customizable stretch and multi-cloud IBM Spectrum LSF cluster configurations that enable users to more quite simply and readily gain the agility, can permeate advantages, and comfort provided by means of cloud computing.

IBM Spectrum LSF is a component of a comprehensive Suite of options supporting commonplace HPC and lofty throughput environments, as well as massive facts, synthetic Intelligence, GPU, laptop discovering, and containerized workloads, amongst many others. IBM Spectrum LSF, the core of the Suite, is an impressive workload administration platform for disturbing, allotted HPC environments. It gives an entire set of clever, coverage-driven scheduling features that assist maximize utilization of compute infrastructure materials whereas optimizing application performance. LSF is the HPC workload management typical, with the most finished set of capabilities – from license scheduling and session scheduling to advanced analytics.

IBM Spectrum LSF Suite comes in three editions and includes a number of extra add-ons reminiscent of IBM Spectrum LSF aid Connector, which allows policy-driven cloud bursting to entire foremost cloud functions, together with IBM Cloud, AWS, Google, and Azure. And now, IBM is providing variable exercise licensing for IBM Spectrum LSF which capacity that LSF clients may live in a position to hasten the reply essentially anywhere, and pay best for what they use.

The cloud presents many merits, however constructing efficacious hybrid cloud solutions isn’t so standard – formerly. With the leading-facet functionality offered through IBM Spectrum LSF and the aid of the brand unique LSF deployment e bespeak for AWS, HPC amenities are tons extra able to manufacture cloud soar for them.

learn about main-side HPC and AI solutions on the IBM booth subsequent week at SC18 (booth #3433) in Dallas, Texas. Register right here for technical briefings and user community sessions.

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

a better issue on the deployment ebook and aiding substances for structure IBM Spectrum LSF hybrid-cloud configurations with Amazon internet functions can too live organize birthright here.


000-N07 IBM Optimization Technical Mastery Test v1

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: 30 true Questions

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

Ricardo Balduino and Tim Bohn

Early Flight, Creative Commons Introduction

As they described in piece 1 of this series, their objective is to assist forecast the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. They exercise historical flights data and historical weather data to manufacture predictions for upcoming flights.

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

Tools used in this exercise case solution

IBM SPSS Modeler is designed to assist ascertain patterns and trends in structured and unstructured data with an intuitive visual interface supported by advanced analytics. It provides a purview of advanced algorithms and analysis techniques, including text analytics, entity analytics, decision management and optimization to deliver insights in near real-time. For this exercise 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 too 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 propound one possible solution for this problem. It’s not meant to live the only or the best possible 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 embolden 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 pattern 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 pattern 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: inception 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 pattern 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

· effect 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, hasten models, evaluate — and attain 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 care 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 organize in their input data set

This is an engaging exercise case, and often a hard one to solve, due to the imbalanced data it presents, as seen in pattern 5. By “imbalanced” they connote that there were far more non-cancelled flights in the historical data than cancelled ones. They will debate 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, inception and destination airport codes, and weather conditions), and which one was the target to live generated by the model (i.e. forecast 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 hasten multiple models at once and preview their outputs, such as the area under the ROC curve, as seen in pattern 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 exercise the Random Trees model since the initial analysis showed it has the best area 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 work more effectively.

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

In pattern 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 exercise unique data, that is, data that was not piece of the original training and testing data sets. Another advantage of this method — in particular for their exercise case — is its faculty to wield imbalanced data.

Since in this exercise 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 pattern 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 amend most of the time, but too made wrong predictions about 4–5% of the time (false negatives and unfounded positives).

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

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

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

Deploying the model

Finally, they deployed the model as a relaxation 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 too 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 manufacture the prediction.

A typical scoring endpoint provided by the Watson Machine Learning service would leer enjoy 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 live cancelled or not. This is seen in pattern 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 live 88.8% likely to live cancelled, based on forecast weather conditions.

Conclusion

IBM SPSS Modeler is a powerful tool that helped us visually create a solution for this exercise case without writing a single line of code. They were able to succeed 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 too find instructions on how to download IBM SPSS Modeler, procure a key for The Weather Channel APIs, and much more.


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

M&AIoT-focused memory 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 traffic 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 separate 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 liquidate die with manufacturing defects and resulting test escapes that cause territory failures, as well as electro-thermal and aging analysis to obviate thermal overstress and forecast 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 embrace a Unit Linting function that runs ALINT-PRO in the background and support for the latest extensions to VHDL.

XJTAG uncorked free DFT software for users of Mentor’s Xpedition product to enlarge the design for test and debug capabilities of the schematic capture and PCB design environment. XJTAG DFT helper helps validate amend 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 development 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 kick 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 touch applications, such as in smartphones. The trouble ties together several MIPI specifications for touch, sensor, and software integration: MIPI Touch Command Set (MIPI TCS) for writing device drivers across operating systems; MIPI Touch Adaptation Layer for I3C (MIPI ALI3C) to translate touch commands for exercise on the MIPI I3C protocol; MIPI I3C Host Controller Interface (MIPI I3C HCI), which enables touch commands and multiple data streams for advanced systems; and MIPI I3C v1.0, which connects an application processor to a touch sensor.

DealsRambus and GigaDevice formed a joint venture, Reliance Memory, in China to focus on development and commercialization of Resistive Ram (RRAM), a non-volatile memory that works by changing the resistance of materials when different voltages are applied. The joint venture has investment funding from THG Ventures, West Summit 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 lofty throughput memory subsystem solution that uses NetSpeed’s interconnect IP and Northwest Logic’s HBM2 and GDDR6 memory 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 memory 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 exercise the funds to accelerate market deployment and adoption of its technology. SiFive is a provider of RISC-V IP cores, SoCs, and development boards, as well a RISC-V-focused IP catalog.

Rambus released fiscal 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 standard this year; under the previous reporting standard, revenue for Q1 2018 was $100.5 million, up 4% from the very quarter ultimate 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 memory and interface IP cores.

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

Cisco refused to participate in NSS Labs report on SD-WAN | killexams.com true 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 judgement 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 judgement given other than, effectively, they didn't want to live tested (for the NSS Labs report)."

Cisco's action marked the first time a vendor had refused to turn on a product NSS Labs had bought for evaluation, Phatak said. Cisco's Viptela team had initially told NSS Labs it would support the test, which led the firm 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 work with."

Cisco's response

After initially refusing to debate 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 true 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 connote the vendor will obviate the test from going forward. Typically, when a company refuses to connect a test, NSS Labs will conduct it alone.

"If someone says they don't want to live tested, they say, 'That's great, but if a product is dependable enough to live sold to the public, it's dependable enough to live 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 procure 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 live able to fill the conversation and procure their money back."

Typically, NSS Labs buys products, and the vendors turn them on enjoy 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 eminent 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 too planned to embrace Silver Peak in the comparison but eminent 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 deem 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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