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No outcome discovered, are trying fresh key phrase!however, searching from the technical charting standpoint ... the SVP and CFO of IBM, said within the revenue muster that the company has been looking for to better its "body of workers optimization productivity ...

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

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

The big majority of agencies at the present time are leveraging the agility, can charge benefits, and convenience offered by means of the common public cloud services mannequin. HPC amenities are furthermore exploring the advantages of cloud, however most likely no longer for the identical motives. Whereas extra objective 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 every unique sound like very first rate concepts, but making them work, effectively and reliably, has been a real problem. Cloud compute components, unlike cloud storage, may furthermore live extra expensive than on-premises infrastructure. security is at every unique times a consideration. relocating colossal HPC statistics units over networks may furthermore live prohibitively gradual and cumbersome. and you deserve to possess deserve to possess purposes that can office 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 redress passage to construct an HPC hybrid cloud computing cluster. each website is diverse, and clients race different workloads that possess very diverse useful resource consumption patterns. for example, some abysmal gaining learning of training consumes colossal quantities of facts to supply a wee 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 necessity to run, however two hybrid architectures commonly upward thrust to the arrogate when IT architects exploit the challenges of creating cloud hover 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 champion 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 possess a compute cluster in yet another location – both on-premises or configured in a cloud. This architecture is formally defined as a unique 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 distinguished deal less difficult in concept than a multi-cluster, this capacity that every one communication and coordination with the grasp scheduler happens over the WAN, which can furthermore 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 passage 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 dwelling 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 swirl 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 upshot I definitely construct 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 consume of IBM Spectrum LSF with Amazon internet services (AWS). This LSF cloud deployment guide builds on an existing relationship with AWS. IBM is proposing abilities, functions, and administration capabilities in an exertion to supply IBM Spectrum LSF users quickly, elastic entry to AWS offerings.

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

IBM Spectrum LSF is a component of a comprehensive Suite of options supporting familiar HPC and towering 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 every unique foremost 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 clients may live in a position to race the reply essentially anywhere, and pay best for what they use.

The cloud presents many merits, however constructing effective hybrid cloud solutions isn’t so standard – formerly. With the leading-facet functionality offered through IBM Spectrum LSF and the aid of the brand fresh LSF deployment e bespeak for AWS, HPC amenities are tons extra able to construct cloud hover 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 appear on the deployment ebook and aiding substances for pile IBM Spectrum LSF hybrid-cloud configurations with Amazon internet functions can furthermore live create prerogative here.


000-N07 IBM Optimization Technical Mastery Test v1

Study guide Prepared by Killexams.com IBM Dumps Experts


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Test Code : 000-N07
Test name : IBM Optimization Technical Mastery Test v1
Vendor name : IBM
: 30 real Questions

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Unfriendly Skies: Predicting Flight Cancellations Using Weather Data, fragment 2 | killexams.com real 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 assist call 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 construct predictions for upcoming flights.

Over the course of this four-part series, they consume different platforms to assist us with those predictions. Here in fragment 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 assist ascertain patterns and trends in structured and unstructured data with an intuitive visual interface supported by advanced analytics. It provides a sweep of advanced algorithms and analysis techniques, including text analytics, entity analytics, conclusion 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 furthermore 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 viable solution for this problem. It’s not meant to live the only or the best viable 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 encourage the readers to try and approach 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 every unique 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 figure 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 figure 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 fresh 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 figure 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

· discharge 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, race models, evaluate — and upshot 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 create in their input data set

This is an appealing consume case, and often a difficult one to solve, due to the imbalanced data it presents, as seen in figure 5. By “imbalanced” they live essential 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, inception and destination airport codes, and weather conditions), and which one was the target to live generated by the model (i.e. call 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 race multiple models at once and preview their outputs, such as the area under the ROC curve, as seen in figure 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 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 figure 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 recumbent to overfitting, which gives a higher likelihood of repeating the identical test results when you consume fresh 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 consume case — is its competence to exploit 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 figure 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 redress most of the time, but furthermore made wrong predictions about 4–5% of the time (false negatives and incorrect positives).

That was the precision given by the model using the training data set. This is furthermore represented by the ROC curve on the left side of figure 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 figure 9), which means that during testing, the model did not discharge as well as during training (i.e. it presented a higher rate of errors, or higher rate of incorrect negatives and incorrect positives).

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

Nevertheless, they decided that the results were silent trustworthy for the purposes of their discussion in this blog, and they stopped their iterations here. They encourage 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 ease 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 furthermore 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 construct the prediction.

A typical scoring endpoint provided by the Watson Machine Learning service would sight like 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 body 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 figure 10, which shows a muster being made to the scoring endpoint — and its response — using an HTTP requester implement 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 implement that helped us visually create a solution for this consume case without writing a unique line of code. They were able to ensue 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 furthermore find instructions on how to download IBM SPSS Modeler, net a key for The Weather Channel APIs, and much more.


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

M&AIoT-focused reminiscence 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 part company focused on smart medical devices.

ToolsCadence debuted a fresh design-for-reliability implement for analog and mixed-signal IC design. The Legato Reliability Solution incorporates analog defect analysis and simulation to evaluate the competence to purge die with manufacturing defects and resulting test escapes that occasions bailiwick failures, as well as electro-thermal and aging analysis to preclude thermal overstress and call 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 involve 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 expand the design for test and debug capabilities of the schematic capture and PCB design environment. XJTAG DFT coadjutant helps validate redress 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 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 feel applications, such as in smartphones. The exertion ties together several MIPI specifications for touch, sensor, and software integration: MIPI feel Command Set (MIPI TCS) for writing device drivers across operating systems; MIPI feel Adaptation Layer for I3C (MIPI ALI3C) to translate feel commands for consume on the MIPI I3C protocol; MIPI I3C Host Controller Interface (MIPI I3C HCI), which enables feel commands and multiple data streams for advanced systems; and MIPI I3C v1.0, which connects an application processor to a feel 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 reminiscence 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 towering throughput reminiscence subsystem solution that uses NetSpeed’s interconnect IP and Northwest Logic’s HBM2 and GDDR6 reminiscence controllers for customers designing SoCs for hyperscale and automotive applications. The companies will provide a common platform to extend NetSpeed’s dynamic-priority elevation approach to the reminiscence 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 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 fresh reporting standard this year; under the previous reporting standard, revenue for Q1 2018 was $100.5 million, up 4% from the identical quarter ultimate 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 reminiscence and interface IP cores.

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

Cisco refused to participate in NSS Labs report on SD-WAN | killexams.com real 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 hollow in its recent comparative report on SD-WAN vendors.

Cisco did not provide a intuition 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 intuition 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 swirl 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 work 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 real 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 live essential the vendor will preclude the test from going forward. Typically, when a company refuses to join 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 trustworthy enough to live sold to the public, it's trustworthy 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 net the money back without going to court.

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

Typically, NSS Labs buys products, and the vendors swirl them on like 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 furthermore planned to involve 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 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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