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: 30 existent Questions

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There comes a degree where respectable ideas don't appear to live satisfactory; conceivable, proven solutions must assume flight.

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

The stately majority of businesses at the present time are leveraging the agility, can saturate benefits, and solace offered by way of the public cloud services mannequin. HPC amenities are additionally exploring the benefits of cloud, but most likely no longer for the selfsame 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 watch to live exploring the chances of accommodating spikes admired with the aid of dynamically adding and subtracting cloud-primarily based substances.

These complete sound fancy very estimable concepts, but making them work, with no care and reliably, has been a existent problem. Cloud compute materials, in contrast to cloud storage, will too live extra costly than on-premises infrastructure. security is always a consideration. stirring colossal HPC records units over networks can too live prohibitively gradual and cumbersome. and you requisite to hold requisite to hold 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 evade distinctive workloads that hold very different aid consumption patterns. as an instance, some abysmal discovering practicing consumes large amounts of information to provide a minuscule mannequin. Others are especially CPU intensive, or GPU intensive. The hybrid cluster solution truly carried out will of direction live pushed by means of the workloads HPC users want to run, however two hybrid architectures often upward thrust to the desirable when IT architects tackle the challenges of constructing cloud coast 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 support 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 hold a compute cluster in a different vicinity – either on-premises or configured in a cloud. This structure is formally described as a sole cluster stretched over a wide enviornment network (WAN) in order that compute nodes on the other belt or within the cloud communicate with a master scheduling host on the originating location. notwithstanding a estimable 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 too live a source of added can saturate or decreased reliability.

A multi-cluster hybrid cloud configuration is a greater knotty 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 revolve 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 consequence I truly obtain this issue fly? A concurrent 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 engage builds on an current relationship with AWS. IBM is featuring potential, features, and management capabilities in an pains to give IBM Spectrum LSF clients quickly, bendy access to AWS offerings.

the modern deployment engage offers information for edifice 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 saturate benefits, and convenience provided through cloud computing.

IBM Spectrum LSF is fragment of a comprehensive Suite of solutions supporting traditional HPC and immoderate throughput environments, in addition to stately 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 support 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 complete fundamental cloud functions, together with IBM Cloud, AWS, Google, and Azure. And now, IBM is providing variable employ licensing for IBM Spectrum LSF which capacity that LSF users should live in a position to evade the solution essentially any place, and pay best for what they use.

The cloud offers many merits, but constructing effectual 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 modern LSF deployment e engage for AWS, HPC facilities are a total lot greater able to obtain cloud coast 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 emerge on the deployment e engage and assisting substances for constructing IBM Spectrum LSF hybrid-cloud configurations with Amazon internet functions can too live 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

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

Ricardo Balduino and Tim Bohn

Early Flight, Creative Commons Introduction

As they described in fragment 1 of this series, their objective is to serve predict the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. They employ historical flights data and historical weather data to obtain predictions for upcoming flights.

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

Tools used in this employ case solution

IBM SPSS Modeler is designed to serve determine patterns and trends in structured and unstructured data with an intuitive visual interface supported by advanced analytics. It provides a orbit of advanced algorithms and analysis techniques, including text analytics, entity analytics, determination management and optimization to deliver insights in near real-time. For this employ 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 pose one workable solution for this problem. It’s not meant to live 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 arrive 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 complete 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 modern columns: source 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, evade models, evaluate — and consequence 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 absorbing employ case, and often a hard one to solve, due to the imbalanced data it presents, as seen in pattern 5. By “imbalanced” they denote that there were far more non-cancelled flights in the historical data than cancelled ones. They will argue 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, source and destination airport codes, and weather conditions), and which one was the target to live generated by the model (i.e. predict 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 evade multiple models at once and preview their outputs, such as the belt 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 employ the Random Trees model since the initial analysis showed it has the best belt 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 toil 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 selfsame test results when you employ modern data, that is, data that was not fragment of the original training and testing data sets. Another advantage of this method — in particular for their employ case — is its skill to exploit imbalanced data.

Since in this employ 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 fallacious 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 belt under the curve for the training data set was better than the belt 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 fallacious negatives and fallacious positives).

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

Nevertheless, they decided that the results were still estimable 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 employ other models that could decipher this employ case.

Deploying the model

Finally, they deployed the model as a repose API that developers can muster 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 muster 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 obtain the prediction.

A typical scoring endpoint provided by the Watson Machine Learning service would inspect fancy 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 carcass 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 muster being made to the scoring endpoint — and its response — using an HTTP requester instrument 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 instrument that helped us visually create a solution for this employ case without writing a sole 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, pick up 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 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 business 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 divide company focused on smart medical devices.

ToolsCadence debuted a modern design-for-reliability instrument for analog and mixed-signal IC design. The Legato Reliability Solution incorporates analog defect analysis and simulation to evaluate the skill to eradicate die with manufacturing defects and resulting test escapes that occasions territory failures, as well as electro-thermal and aging analysis to prevent thermal overstress and predict 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 role 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 augment the design for test and debug capabilities of the schematic capture and PCB design environment. XJTAG DFT assistant 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 progress 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 handle applications, such as in smartphones. The pains ties together several MIPI specifications for touch, sensor, and software integration: MIPI handle Command Set (MIPI TCS) for writing device drivers across operating systems; MIPI handle Adaptation Layer for I3C (MIPI ALI3C) to translate handle commands for employ on the MIPI I3C protocol; MIPI I3C Host Controller Interface (MIPI I3C HCI), which enables handle commands and multiple data streams for advanced systems; and MIPI I3C v1.0, which connects an application processor to a handle sensor.

DealsRambus and GigaDevice formed a joint venture, Reliance Memory, in China to focus on progress 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 acme 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 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 employ the funds to accelerate market deployment and adoption of its technology. SiFive is a provider of RISC-V IP cores, SoCs, and progress 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 participate of $0.36; on a non-GAAP basis there was a loss per participate of $0.10. Rambus changed to a modern reporting standard this year; under the previous reporting standard, revenue for Q1 2018 was $100.5 million, up 4% from the selfsame quarter final year. GAAP income per participate for the quarter was $0.05, up 66.7% from $0.03 in Q1 2017; non-GAAP income per participate 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 scholarship headquarters 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 reason 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 reason 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 revolve 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 hard 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 toil with."

Cisco's response

After initially refusing to argue 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 denote the vendor will prevent 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 live tested, they say, 'That's great, but if a product is estimable enough to live sold to the public, it's estimable 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 pick up the money back without going to court.

"I hope it doesn't arrive to that," Phatak said. "We haven't talked to any lawyers. I'm assuming that we'll live able to hold the conversation and pick up their money back."

Typically, NSS Labs buys products, and the vendors revolve them on fancy 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 celebrated 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 celebrated 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 consider 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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Our ISBN13 : 9781260092332
Subject : Business & Economics
Price : $80.00
Understanding BusinessUnderstanding Business
By William Nickels, James McHugh, Susan McHugh
Publisher : McGraw-Hill (Jan 2018)
ISBN10 : 1260277143
ISBN13 : 9781260277142
Our ISBN10 : 126009233X
Our ISBN13 : 9781260092332
Subject : Business & Economics
Price : $77.00
Understanding BusinessUnderstanding Business
By William Nickels, James McHugh, Susan McHugh
Publisher : McGraw-Hill (Jan 2018)
ISBN10 : 1259929434
ISBN13 : 9781259929434
Our ISBN10 : 126009233X
Our ISBN13 : 9781260092332
Subject : Business & Economics
Price : $76.00
P2050-007P2050-007
By Peter W. Cardon
Publisher : McGraw-Hill (Jan 2017)
ISBN10 : 1260128474
ISBN13 : 9781260128475
Our ISBN10 : 1259921883
Our ISBN13 : 9781259921889
Subject : Business & Economics, Communication & Media
Price : $39.00
P2050-007P2050-007
By Peter Cardon
Publisher : McGraw-Hill (Feb 2017)
ISBN10 : 1260147150
ISBN13 : 9781260147155
Our ISBN10 : 1259921883
Our ISBN13 : 9781259921889
Subject : Business & Economics, Communication & Media
Price : $64.00
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