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000-202 - Enterprise Storage Technical Support(R) Specialist V1 - Dump Information

Vendor : IBM
Exam Code : 000-202
Exam Name : Enterprise Storage Technical Support(R) Specialist V1
Questions and Answers : 59 Q & A
Updated On : December 14, 2018
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000-202 Questions and Answers

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000-202 Enterprise Storage Technical Support(R) Specialist V1

Study Guide Prepared by Killexams.com IBM Dumps Experts


Killexams.com 000-202 Dumps and Real Questions

100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



000-202 exam Dumps Source : Enterprise Storage Technical Support(R) Specialist V1

Test Code : 000-202
Test Name : Enterprise Storage Technical Support(R) Specialist V1
Vendor Name : IBM
Q&A : 59 Real Questions

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IBM IBM Enterprise Storage Technical

Q&A: IBM Flash Storage techniques CTO Andy walls | killexams.com Real Questions and Pass4sure dumps

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IBM reinforces commercial enterprise multicloud growth with automation tools, ServiceNow expansion | killexams.com Real Questions and Pass4sure dumps

IBM is asking to make it simpler for consumers to move to multicloud environments by adding automation tools to its cloud capabilities, and the company is extending its relationship with cloud migration specialists ServiceNow.

The riding thought at the back of each strikes is to assist consumer simplify what may also be a daunting assignment – moving new and legacy functions to multicloud environments be they according to IBM's own cloud carrier or others akin to Amazon internet functions, Google Cloud, Microsoft Azure and crimson Hat.

As a backdrop to the new capabilities, IBM closing month pointed out it would purchase open-source software pioneer crimson Hat in a $34 billion stock acquisition. For IBM the deal may imply many things. It makes it a bigger open source and commercial enterprise utility participant as an example, but in the main it receives large Blue into the lucrative hybrid-cloud party targeting its towering rivals Google, Amazon and Microsoft amongst others. Gartner says that market may be value $240 billion with the aid of subsequent year.

a standard cloud manner requires re-hosting, re-platforming and re-architecting that can take weeks to just a few months counting on the readiness and quantity of applications to migrate. In creating hybrid and multicloud environments, companies face the challenge of needing to circulate and manage high volumes of facts, capabilities and workflows with out inflicting enterprise disruption, IBM says.

And that’s the place the brand new capabilities come in. IBM has rolled out new automation elements to its Cloud Migration manufacturing unit carrier that big Blue says will assist purchasers reduce the time it takes to migrate and modernize applications for a multicloud or hybrid cloud deployment. IBM Cloud Migration manufacturing facility usually handles records validation including counsel on the source environment, migration-server setup, server inventory, technical specifications, functions, access credentials and creating a migration plan.

the new toolset comprises algorithms derived “from years of complex migration and modernizations that support enhance planning accuracy and reduce hazards through uncovering dependencies and technical and system challenges a lot previous within the lifecycle,” IBM says. With these capabilities, IBM Cloud Migration manufacturing unit allows for smoother and sooner cloud adoption throughout dissimilar platforms, the business says.

Easing the move to hybrid and multicloud environments is the idea at the back of IBM extending its carrier family members with ServiceNow. both corporations teamed up in 2017 with a plan to mix ServiceNow’s ’s cloud-based mostly carrier-automation platform and IBM cloud products and features.

today’s announcement mixes IBM’s recently announced Multicloud administration Platform with ServiceNow's IT provider administration and IT Operations administration capabilities.

announced in October, IBM’s Kubernetes-primarily based Multicloud manager runs on the business’s year-old IBM Cloud private platform but lets customers manage and integrate workloads on clouds from other providers corresponding to Amazon, Microsoft and red Hat. on the coronary heart of the Multicloud supervisor is a dashboard interface for managing lots of Kubernetes applications and big volumes of records even with the place within the organization they can be found.

IBM says the extended ServiceNow partnership will aid shoppers with:

  • Agile software building and comprehensive services for managing cloud lifecycle and business resources.
  • The potential to manage heterogeneous cloud environments quicker, greater can charge-readily and with improved consistency.
  • Visibility into multi-cloud operations to manage provider health and assist optimize service birth and charges.
  • join the community World communities on facebook and LinkedIn to touch upon topics that are suitable of intellect.

    IBM (IBM) Down 10.three% considering final revenue document: Can It Rebound? | killexams.com Real Questions and Pass4sure dumps

    A month has gone by for the reason that the remaining revenue document for IBM (IBM). Shares have lost about 10.three% in that time frame, underperforming the S&P 500.

    Will the contemporary negative style continue leading as much as its next profits release, or is IBM due for a breakout? before we dive into how buyers and analysts have reacted as of late, let's take a short look on the most contemporary profits report so as to get a far better deal with on the important catalysts.

    IBM’s Q2 results advantage from can charge slicing, reduce Share count

    IBM said third-quarter 2018 non-GAAP revenue of $three.42 per share, which beat the Zacks Consensus Estimate via couple of cents. income per share (EPS) improved 4.9% from the year-in the past quarter.

    The 12 months-over-12 months increase in EPS can also be attributed to strong pre-tax margin operating leverage (28 cents contribution) and aggressive share buybacks (19 cents contribution). This turned into partly offset by lower revenues (seven cents poor have an effect on) and better tax cost (17 cents negative impact).

    Revenues of $18.seventy six billion lagged the Zacks Consensus Estimate of $19.10 billion and declined 2.1% on a 12 months-over-12 months basis. At regular forex (cc), revenues remained flat.

    IBM stated that signings plunged 21% to $8 billion. capabilities backlog declined three% from the 12 months-in the past quarter to $113 billion.

    Geographic income details

    Revenues from Americas inched up 1%, driven through endured increase in Canada and Latin the usa and modest increase in the united states.

    Europe, core-East and Africa reduced 2% from the year-in the past quarter, pushed by using decline in Germany and France, partially offset through increase in Spain and the United Kingdom.

    Asia-Pacific revenues declined 1% on a 12 months-over-year groundwork with modest boom in Japan.

    Strategic Imperatives boom Continues

    Strategic Imperatives (cloud, analytics, mobility and safety) grew 7% at cc from the yr-ago quarter to $9.three billion. safety revenues surged 34%. On a trailing 12-month groundwork, Strategic Imperatives revenues had been $39.5 billion, up 13% (11% at cc).

    Cloud revenues surged 13% from the year-in the past quarter to $4.6 billion. The annual run rate for cloud as-a-service revenues expanded 24% at cc on a 12 months-over-year basis to $eleven.4 billion.

    Cloud revenues of $19 billion on a trailing 12-month basis improved 20% (18% at cc) and now debts for twenty-four% of IBM’s total revenues.

    Cognitive Revenues Decline

    Cognitive solutions’ revenues-exterior lowered 5.7% 12 months over year (down 5% at cc) to $4.15 billion. Segmental revenues referring to Strategic Imperatives and Cloud declined 4% and a couple of%, respectively. Cloud as-a-carrier earnings annual run rate changed into $2 billion.

    solutions utility contains choices in strategic verticals like fitness, domain-specific capabilities like analytics and security, and IBM’s rising applied sciences of AI and blockchain. The phase additionally contains offerings that address horizontal domains like collaboration, commerce and talent. options software revenues lowered 3% yr over yr within the quarter.

    IBM pointed out that in commerce area the infusion of AI into choices like consumer journey analytics helped SaaS signings to grow double digit in the quarter. The recent launch of Notes Domino version 10, which is optimized for cell, and supports JavaScript and node.js will raise boom collaboration in 2019.

    Transaction Processing software contains application that runs mission-important workloads, leveraging IBM’s hardware systems. Revenues fell eight% on a year-over-year groundwork.

    IBM witnessed increase in industry verticals like health, key areas of analytics and protection within the quarter. Watson health witnessed vast-primarily based boom in Payer, company, Imaging and life Sciences domains.

    right through the quarter, the Sugar.IQ software, developed by way of Medtronic in partnership with IBM, hit the market. The utility is designed to simplify and increase daily diabetes administration.

    IBM brought up that analytics carried out smartly within the quarter, pushed by means of information science choices and IBM Cloud inner most for records offering.

    all through the quarter, the business announced bias detection functions and launched new Watson services on the IBM Cloud deepest platform.

    safety increase turned into pushed by way of offerings in orchestration, information protection and endpoint management.

    In blockchain, IBM meals have faith network for meals safeguard went are living in the quarter. Reatiler Carrefour joined IBM’s blockchain network. The enterprise additionally collectively introduced TradeLens with Maersk that addresses inefficiencies within the international supply chain. IBM currently supports 75 energetic blockchain networks.

    international enterprise functions Revenues increase

    Revenues from international business features-external section had been $4.13 billion, up 0.9% from the year-in the past quarter (up 3% at cc). Segmental revenues pertaining to Strategic Imperatives grew 9%. Cloud observe surged 18%. Cloud as-a-provider income annual run cost become $1.9 billion.

    application administration revenues declined 1% from the 12 months-ago quarter. besides the fact that children, international manner capabilities revenues climbed 2%. in addition, Consulting revenues accelerated 7% yr over yr, driven through strong performance from IBM’s digital enterprise.

    technology functions & Cloud structures: Revenues Dip

    Revenues from expertise functions & Cloud systems-exterior diminished 2% from the year-ago quarter (flat at cc) to $eight.29 billion. Segmental revenues pertaining to Strategic Imperatives superior 16%, driven by using hybrid cloud capabilities. Cloud surged 22% from the 12 months-in the past quarter. Cloud as-a-service earnings annual run fee become $7.5 billion.

    Integration application increased 1% from the yr-in the past quarter. all the way through the quarter, 95 companies around the world chosen IBM Cloud inner most offering. Infrastructure capabilities revenues also multiplied 1% on a year-over-yr groundwork.

    besides the fact that children, Technical help services revenues decreased 3% from the 12 months-in the past quarter.

    vigor & z14 power methods Revenues

    programs revenues multiplied 0.9% on a yr-over-yr groundwork (up 2% at cc) to $1.74 billion. Segmental revenues touching on Strategic Imperatives surged 5%, whereas Cloud revenues declined 8%.

    IBM Z revenues accelerated 6% 12 months over 12 months on more than 20% MIPS increase, pushed by using vast-based adoption of the z14 mainframe.

    energy revenues elevated 17% from the 12 months-in the past quarter. all over the quarter, IBM launched its next technology POWER9 processors for midrange and excessive-end systems that are designed for dealing with superior analytics, cloud environments and data-intensive workloads in AI, HANA, and UNIX markets.

    IBM additionally introduced new choices optimizing both hardware and utility for AI. management believes that products like PowerAI imaginative and prescient and PowerAI enterprise will support power new customer adoption.

    besides the fact that children, storage hardware revenues declined 6% due to vulnerable performance within the midrange and excessive conclusion, partly offset by means of strong boom in All Flash Arrays. IBM mentioned that pricing force in the immensely aggressive storage market is hurting revenues. The company introduced its new FlashSystems with subsequent generation NVMe expertise right through the quarter.

    operating techniques software revenues declined 4%, while techniques Hardware superior 4% from the 12 months-in the past quarter.

    at last, world Financing (includes financing and used machine income) revenues diminished 9.1% at cc to $388 million.

    operating particulars

    Non-GAAP gross margin remained unchanged from the year-in the past quarter at 47.4%. This was IBM’s optimal gross margin efficiency in years and turned into basically pushed by using 160 basis points (bps) expansion in capabilities margin. however, detrimental combine in z14 mainframe and utility totally offset this expansion.

    working price declined four% yr over year, because of awareness of acquisition synergies and enhancing operational efficiencies. IBM continues to invest in quickly growing to be fields like hybrid cloud, synthetic intelligence (AI), security and blockchain.

    Pre-tax margin from carrying on with operations increased 50 bps on a yr-over-year foundation to 19.2%.

    Cognitive solutions and global business features section pre-tax margins increased 190 bps and 320 bps, respectively, on a yr-over-year groundwork. however, expertise capabilities & Cloud platforms phase pre-tax margin gotten smaller one hundred bps.

    techniques pre-tax revenue was $209 million down 38% yr over year. global Financing phase pre-tax salary jumped 26.7% to $308 million.

    balance Sheet & cash circulation details

    IBM ended third-quarter 2018 with $14.70 billion in complete money and marketable securities in comparison with $11.93 billion at the conclusion of second-quarter 2018. complete debt (including international financing) became $forty six.9 billion, up $1.4 million from the outdated quarter.

    IBM stated money circulation from operations (except for global Financing receivables) of $3.1 billion and generated free money circulation of $2.2 billion in the quarter.

    in the said quarter, the company returned $2.1 billion to shareholders via dividends and share repurchases. on the end of the quarter, the business had $1.four billion remaining beneath present buyback authorization.

    suggestions

    IBM reiterated EPS forecast for 2018. Non-GAAP EPS is anticipated to be at the least $13.eighty.

    IBM still anticipates 2018 free cash stream of $12 billion.

    Story Continues

    How Have Estimates Been moving since Then?

    in the past month, buyers have witnessed a downward trend in clean estimates.

    VGM ratings

    at this time, IBM has a typical growth score of C, although it's lagging a bit on the Momentum ranking entrance with a D. however, the inventory changed into allocated a grade of A on the price facet, placing it within the exact quintile for this funding method.

    basic, the inventory has an aggregate VGM score of B. in case you aren't focused on one method, this score is the one you should be drawn to.

    Outlook

    Estimates were largely trending downward for the inventory, and the magnitude of those revisions shows a downward shift. in particular, IBM has a Zacks Rank #3 (cling). We are expecting an in-line return from the inventory in the following few months.

    desire the newest recommendations from Zacks investment research? these days, that you would be able to download 7 most desirable shares for the next 30 Days. click to get this free report foreign business Machines business enterprise (IBM) : Free inventory analysis report To read this text on Zacks.com click on right here. Zacks investment research


    000-202 Enterprise Storage Technical Support(R) Specialist V1

    Study Guide Prepared by Killexams.com IBM Dumps Experts


    Killexams.com 000-202 Dumps and Real Questions

    100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



    000-202 exam Dumps Source : Enterprise Storage Technical Support(R) Specialist V1

    Test Code : 000-202
    Test Name : Enterprise Storage Technical Support(R) Specialist V1
    Vendor Name : IBM
    Q&A : 59 Real Questions

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    Xenomorph Extends EDM NoSQL Integration with MarkLogic® Database | killexams.com real questions and Pass4sure dumps

    NEW YORK & LONDON & SINGAPORE--(BUSINESS WIRE)--Xenomorph, the data and analytics people, today announced that its TimeScape EDM+, Xenomorph’s enterprise data management (EDM) solution for financial markets, has been integrated with the MarkLogic® database, a leading operational and transactional Enterprise NoSQL database.

    TimeScape EDM+ offers ready-to-run data integration, cleansing, validation, audit, analysis and distribution for reference, market and derived data. Clients can leverage all of this functionality integrated with the ACID compliant, scale-out, bitemporial capabilities of the MarkLogic database. Both structured and unstructured data stored in the MarkLogic database can be integrated with data and analytics in TimeScape EDM+, enabling business users to access, validate and analyse these combined datasets without any specialist technical knowledge. A user can access data through one family of consistent TimeScape EDM+ interfaces whether their preference is Excel, a programming API like Python, BI platforms like Tableau and Power BI, or mathematical environments like Matlab and R.

    “The integration of TimeScape EDM+ with the MarkLogic database continues our efforts to explore the benefits of NoSQL and scale-out SQL technologies applied to EDM in financial markets,” says Chris Budgen, Chief Technical Architect, Xenomorph. “The combination of document and semantic storage within the MarkLogic database is an interesting fit with the TimeScape database model, enabling easy support of new asset classes through schema flexibility combined with easy definition of relationships between different financial objects, such as those from a curve to its underlying data points.”

    “The data management and analytical needs of financial markets customers continues to be challenging due to the competitive and regulatory environment. This partnership will help the financial service providers to address those requirements,” said David Ponzini, Senior Vice President, Corporate Development and Marketing, MarkLogic Corporation. “The combination of our enterprise-strength, flexible database platform and TimeScape EDM+ offers a robust, agile, solution that rationalizes data logistics and improves time-to-market deployment.”

    “The integration of TimeScape EDM+ with the MarkLogic database extends the ways in which our clients can manage, validate and analyse data,” adds Brian Sentance, CEO, Xenomorph. “Clients face many data management challenges currently, not least of which are the changes introduced by new regulations such as FRTB and IFRS 9. Delivering data management solutions that have the flexibility to instantly integrate new asset classes, new datasets and new types of data into these new requirements is a key to business success in coming years. TimeScape EDM+ with the MarkLogic database offers one way in which these issues can be addressed through the combination of technical scale and business flexibility.”

    About Xenomorph

    Xenomorph believes that great decisions are made when everyone can easily access and analyze the same high quality data. Based on this belief, we develop software solutions for financial markets that enable both technologists and business users alike to manage, cleanse and analyze more data, more quickly. These solutions include an Enterprise Data Management system, TimeScape EDM+, a cloud-based data publishing service, TimeScape MarketPlace and our underlying data and analytics technology platform, TimeScape. Trading, research, risk, product control, IT and back-office staff use these solutions at investment banks, brokerages, insurers, hedge funds and asset management companies worldwide. Established in 1995, Xenomorph has offices in London, New York and Singapore.

    For more information on Xenomorph, our clients, services and solutions, please visit www.xenomorph.com.

    Xenomorph® and TimeScape™ are registered trademarks of Xenomorph Software Limited. Other product and company names herein may be trademarks of their respective owners.

    About MarkLogic

    For over a decade, organizations around the world have come to rely on MarkLogic to power their innovative information applications. As the world’s experts at integrating data from silos, MarkLogic’s operational and transactional Enterprise NoSQL database platform empowers our customers to build next generation applications on a unified, 360-degree view of their data. Headquartered in Silicon Valley, MarkLogic has offices throughout the U.S., Europe, Asia, and Australia.

    For more information, please visit www.marklogic.com.

    MarkLogic is a registered trademark of MarkLogic Corporation in the United States and/or other countries. All other trademarks mentioned are the property of their respective owners.


    Cisco Announces Open Network Environment to Unleash Application-Driven Network Programmability | killexams.com real questions and Pass4sure dumps

    Cisco Live! San Diego – June 13, 2012 –  Cisco today introduced a versatile and broad approach to network programmability— Cisco ® Open Network Environment (Cisco ONE) —  aimed at helping  customers  drive the next wave of business innovation through trends such as cloud, mobility, social networking, and video. Cisco ONE enables flexible, application-driven customization of network infrastructures to help realize business objectives such as:  increased service velocity, resource optimization, and faster monetization of new services.

    The Cisco Open Network Environment is delivered through a rich set of platform APIs, agents and controllers, and overlay network technologies. Cisco ONE complements current approaches to software-defined networking while encompassing the entire solution stack from transport to management and orchestration.  With Cisco's Open Network Environment customers can harness the intelligent network through programmability and abstraction across multiple layers, offering a choice of protocols, industry standards, and usage-based deployment models. 

    As part of the Open Network Environment, Cisco announced One Platform Kit (onePK) which provides application programming interfaces (APIs) for developers across Cisco operating systems: Cisco IOS ®, IOS-XR, and NX-OS. Cisco also announced proof-of-concept controller software and proof-of-concept OpenFlow agent for Software Defined Networking (SDN) research.  Cisco is also enabling scalable virtual overlay networks for multi-tenant cloud deployments with the Cisco Nexus® 1000V virtual switch. New innovations include: OpenStack support, programmability, multi-hypervisor capability, and VXLAN gateway functionality.

    Cisco is collaborating on emerging network technologies with industry leaders, academic organizations, and standards bodies to meet their heterogeneous requirements for network programmability. Cisco's Open Network Environment supports a wide variety of deployment models including: 

  • Universities and Research Organizations: Network partitioning or "campus network slicing" using proof-of-concept  controller software and OpenFlow agents for SDN research
  • Hyperscale Data centers: Network flow management with programmatic access via APIs
  • Cloud Providers: Automated provisioning and programmable overlay network for scalable multi-tenancy
  • Service Providers: Programmatic access, policy and analytics to optimize and monetize service delivery
  • Enterprises: Private cloud automation for virtual workloads , including VDI
  • NEWS HIGHLIGHTS

    Cisco Open Network Environment is adopting a multi-pronged product strategy to deliver network programmability.

    One Platform Kit (onePK) Software development kit for customers and partners

  • onePK delivers a rich set of APIs for deep programmatic access across Cisco operating systems and hardware platforms. 
  • Cisco will provide phased support for onePK on IOS, IOS-XR, and NX-OS operating systems and hardware platforms including ASR, ISR G2, CRS, Catalyst ® and Nexus® switches.  Cisco onePK support will be provided initially for the ASR1000 and ISRG2.
  • onePK allows tight integration with software applications for greater visibility, orchestration and control of network infrastructure for rapid service creation and delivery, and automate multiple network tasks and processes.     
  • With onePK, developers can select the language they are familiar with, use existing tools, and easily integrate into existing development environments.    
  • On June 12 Cisco announced its Cloud Connected Solution Partners and customers can also develop their own Cloud Connectors using onePK APIs to enhance and differentiate Cloud Services.  Cloud Connector is a software component residing on ISR G2 and ASR1000 routing platforms to improve availability, performance, and security for cloud services.  
  • Cisco Controller Software and OpenFlow Agents for Software Defined Networking (SDN)

  • Cisco will provide proof-of-concept controller software for SDN and is collaborating with academic and research institutions in these efforts. For example, universities will be able to partition or "slice" their campus network to enable academic research on SDN with consistent policy management.
  • Cisco will provide proof-of-concept OpenFlow v1.0 agents on the Cisco Catalyst 3750-X and 3560-X Series Switches.
  • Cisco is also leading several workgroups at the Open Network Foundation.
  • Virtual Overlay Networks extend support for OpenStack, multiple hypervisors,  and VXLAN gateway functionality for consistency across physical and virtual networks

  • Cisco will expand virtual overlay network solutions for scalable, multi-tenant cloud infrastructures and Infrastructure-as-a-Service (IAAS) through enhanced capabilities of its Nexus 1000V portfolio with support for open-source hypervisors.  This adds to existing support for VMware and Microsoft hypervisors.
  • Cisco will also provide the Virtual Extensible Local Area Network (VXLAN)  gateway to bridge traditional VLAN-based physical network environments with VXLAN-based virtual overlay networks.
  • Cisco will extend vPath technology implemented on Nexus 1000V to support virtual services on VXLAN-based overlay networks, including VSG and ASA1000V.  Cisco vPath will steer or "chain" traffic across VSG and ASA1000V through pre-defined policies to simplify operational workflows on a per-tenant basis.
  • Cisco will offer standards-based OpenStack Quantum Plug-in and REST APIs for the Cisco Nexus 1000V virtual switch to rapidly orchestrate multi-tenant cloud infrastructures.
  • Availability

    Beta trials and phased general availability are scheduled to begin the last quarter of 2012.

    SUPPORTING QUOTES

    Cisco:  Padmasree Warrior, CTO, SVP Engineering, GM Enterprise Business   "The Cisco Open Network Environment is key to our vision of an intelligent network that is more open, programmable, and application-aware—  a vision in which the network is transformed into a more effective business enabler," said Padmasree Warrior, Cisco chief technical officer, senior vice president of Engineering, and general manager of Enterprise Business. "With Cisco's Open Network Environment, customers get a comprehensive and robust set of capabilities to address emerging enterprise and service provider requirements to harness network intelligence and provide value for new business platforms in an evolutionary manner."  

    Cisco:  David Yen, SVP and GM of the Data Center Technology Group "Today's announcement is a milestone event that signals Cisco's leadership, contribution and dedication to open networking," said David Yen, senior vice president and general manager of the Data Center Technology Group at Cisco. "Using the Cisco ONE Platform Kit, developers will have access to over 20 years of intelligent networking software produced by Cisco's top R&D talent.  Together, we'll usher in a new era of programmability through transparent and granular interaction between applications and network infrastructure."

    CTERA: Liran Eshel, chief executive officer:  "By leveraging Cisco ISR G2 as a platform, CTERA and Cisco allow customers to bridge the gap between local and cloud storage by combining LAN speed user experience with the resilience of the cloud," said Liran Eshel, chief executive officer at CTERA. "Utilizing the ISR's network capabilities, the consolidated solution can effectively manage cloud storage traffic in an optimized way. The integration, which was rapidly implemented as a connector using Cisco's Open Network Environment and the onePK API, allowed us to deliver the world's first cloud-storage-enabled router with intelligent QoS and superior user experience."

    MLB.com:  Kushal Patel, VP of Networking  "Baseball has dedicated and passionate fans who have high expectations for the quality and value of their interactive experiences, which demands a reliable network," said Kushal Patel, vice president of networking at MLB.com.  "We like the strategic direction Cisco is taking with its Open Network Environment, which provides a platform to more closely integrate customer-built software applications with the network."

    Indiana University:  Brad Wheeler, vice president of information technology and CIO "Indiana University is delighted to have Cisco as the newest member of InCNTRE's SDN Interoperability Lab," said Brad Wheeler, Indiana University vice president of information technology and CIO.  "This collaboration, along with Cisco's vision for dynamic and multi-dimensional networks – the Open Network Environment-  will help drive innovation to advance next generation networks."

    University of Wisconsin Madison:  Bruce Maas, Vice Provost for Information Technology and CIO  "Our IT network engineers and computer science department researchers have been collaborating with Cisco to develop and advance SDN solutions that will help move SDN from the R&D lab to mainstream business and academic production environments," said  Bruce Maas, vice provost for information technology and CIO at University of Wisconsin Madison. We believe that programmable networks—providing program interfaces to devices and software that take advantage of network intelligence-will enable new research innovations that will advance science and boost economic development."  

    HCL Technologies LTD:  Prabhuraman Sayanam, Vice President   "As a leading global IT and software development company, HCL Technologies is always interested in taking advantage of network innovations," said Prabhuraman Sayanam, vice president at HCL Technologies LTD. "As one of the first developer partners for Cisco's onePK, we have already begun working with the application programming interface.  We're looking forward to using Cisco's Open Network Environment and onePK to create innovative new software applications to serve our joint customers' needs."

    SUPPORTING RESOURCES

    About Cisco

    Cisco (NASDAQ: CSCO) is the worldwide leader in networking that transforms how people connect, communicate and collaborate.  Information about Cisco can be found at http://www.cisco.com. For ongoing news, please go to http://newsroom.cisco.com.

    # # #

     Cisco and the Cisco logo are trademarks or registered trademarks of Cisco and/or its affiliates in the U.S. and other countries. A listing of Cisco's trademarks can be found at www.cisco.com/go/trademarks. Third-party trademarks mentioned are the property of their respective owners. The use of the word partner does not imply a partnership relationship between Cisco and any other company.

    # # #


    Fact Sheet: Unleashing the Power of Artificial Intelligence in Healthcare with Intel Technologies | killexams.com real questions and Pass4sure dumps

    Artificial Intelligence: Machine Learning, Deep Learning and More

    Artificial intelligence (AI) is now all around us, as machines increasingly gain the ability to sense, learn, reason, act and adapt in the real world. Much of the AI spotlight is focused on deep learning, a branch of machine learning that uses neural networks to comprehend complex and unstructured data (training) and use that understanding to classify, recognize and process new inputs (inference). Deep learning is delivering breakthroughs in areas like image recognition, speech recognition, natural language processing and other complex tasks.

    Processing Deep Learning Workloads in Healthcare

    Deep learning workloads require a tremendous amount of computational power to run complex mathematical algorithms and process huge amounts of data. While GPUs have been used for some deep learning training applications, they don’t necessarily have a performance edge for deep learning inference. In healthcare in particular, there are many examples of applications that run better on CPUs than GPUs.

    That’s why for healthcare organizations running AI workloads, Intel® Xeon® Scalable processors provide an ideal computational foundation. Intel Xeon Scalable processors are optimized for AI, scale up quickly and seamlessly for 2.1x faster deep learning performance over previous generations1, and offer server-class reliability and workload flexibility1.

    Versatility

    Many organizations that are implementing AI don’t need to train models 24/7. Those that do need to train models around the clock require a dedicated accelerator, but the majority of organizations simply need to train and then deploy a model. If an organization invests thousands of dollars in building a dedicated acceleration stream, it can’t do anything else with that infrastructure beyond training – it will just sit idle.

    Intel Xeon Scalable processors are more agile. They are designed to flex with business needs to support the workload needs of the moment, allowing organizations to leverage their data center infrastructure for AI applications and a wide range of other workloads.

    Increased Utilization

    Most organizations have at least 35 percent free utilization of processing capacity, if not more. This means they have could be obtaining better ROI from their infrastructure investments. With Intel Xeon Scalable processors, they can leverage this unused capacity to implement AI while still meeting the needs of other applications.

    Technical Advantages

    GPUs are often leveraged as an accelerator, but they have limitations when it comes to building models to tackle healthcare issues. These limitations begin with memory. GPUs are limited to only 12 to 16 gigabytes of memory on the chip itself, and that restricts the size and capabilities of the model that can be built. This memory limitation is a particular problem in healthcare AI work, which often involves building massive models – for example, to support a giant CT scan.

    For today’s AI networks deployed on a standard GPU, the image size for a scan that can be developed is usually 256 x 256 pixels. At the most, that image could be 1000 x 1000 pixels, but that’s pushing it. If clinicians have a 4K image where they actually need all of those pixels to make an analysis — for example, to detect a tumor in a tiny portion of the image — then they have a problem. When they scale that 4K image down to 256 x 256 pixels, they lose all of the fine temporal resolution.

    The same model can be built with Intel Xeon Scalable processors without limiting the size and resolution because the Intel Xeon Scalable family offers a terabit of on-load memory. Intel Xeon processors may process the image more slowly than a GPU, but when the resolution is needed for medical imaging, Intel Xeon processors deliver.

    A Processor Designed for Deep Learning

    Moving forward, Intel has also developed the Intel® Nervana™ Neural Network Processor (NNP), the world’s first processor specifically designed from the ground up for deep learning. The Intel Nervana NNP promises to further enhance medical imaging and other healthcare applications.

    Using the Intel Nervana platform, healthcare organizations can develop entirely new classes of AI applications that maximize the amount of data processed and lead to greater insights. For example, AI will allow for earlier diagnosis and greater accuracy, helping make the impossible possible by advancing research on cancer, Parkinson’s disease and other brain disorders2.

    The Intel Advantage for AI

    Intel has 50 years of experience in helping its customers make their data valuable. Today, Intel is using that experience to help healthcare organizations address every aspect of their workflows through the successful implementation of AI.

    From hardware to software and data science, Intel brings its full suite of products and expertise to AI. Intel has the technical expertise not only to help organizations build the right infrastructure – from data to storage and network – but also has the data scientists to understand and model data and the application developers to help make the data useful. In short, Intel’s expertise to implement AI goes well beyond the CPU.

    Driving the Age of AI

    At Intel, we recognize the age of AI is upon us, and we know our technologies will help drive the future of AI. We are also motivated by a desire to use our technology to help advance society and tackle the world’s big challenges, so we’re particularly excited about our work in healthcare. We want to help channel AI for societal good – we call it AI with a purpose.

    With these thoughts in mind, we’re pleased to be engaged with healthcare providers and researchers. We know that our collective work is helping move the needle – with even more promise and unlimited possibilities for the future.

    Benchmark results were obtained prior to implementation of recent software patches and firmware updates intended to address exploits referred to as “Spectre” and “Meltdown”.  Implementation of these updates may make these results inapplicable to your device or system.

    Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors.

    Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit www.intel.com/benchmarks.

    1INFERENCE using FP32 Batch Size Caffe GoogleNet v1 256  AlexNet 256.

    Performance estimates were obtained prior to implementation of recent software patches and firmware updates intended to address exploits referred to as “Spectre” and “Meltdown.”  Implementation of these updates may make these results inapplicable to your device or system. Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors.  Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions.  Any change to any of those factors may cause the results to vary.  You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit http://www.intel.com/performance  Source: Intel measured as of June 2017 Optimization Notice: Intel’s compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

    Configurations for Inference throughput

    Processor :2 socket Intel(R) Xeon(R) Platinum 8180 CPU @ 2.50GHz / 28 cores HT ON , Turbo ON Total Memory 376.46GB (12slots / 32 GB / 2666 MHz).CentOS Linux-7.3.1611-Core , SSD sda RS3WC080 HDD 744.1GB,sdb RS3WC080 HDD 1.5TB,sdc RS3WC080 HDD 5.5TB , Deep Learning Framework caffe version: f6d01efbe93f70726ea3796a4b89c612365a6341 Topology :googlenet_v1 BIOS:SE5C620.86B.00.01.0004.071220170215 MKLDNN: version: ae00102be506ed0fe2099c6557df2aa88ad57ec1 NoDataLayer. Measured: 1190 imgs/sec vs Platform: 2S Intel® Xeon® CPU E5-2699 v3 @ 2.30GHz (18 cores), HT enabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 256GB DDR4-2133 ECC RAM. CentOS Linux release 7.3.1611 (Core), Linux kernel 3.10.0-514.el7.x86_64. OS drive: Seagate* Enterprise ST2000NX0253 2 TB 2.5″ Internal Hard Drive. Performance measured with: Environment variables: KMP_AFFINITY=’granularity=fine, compact,1,0‘, OMP_NUM_THREADS=36, CPU Freq set with cpupower frequency-set -d 2.3G -u 2.3G -g performance. Deep Learning Frameworks: Intel Caffe: (http://github.com/intel/caffe/), revision b0ef3236528a2c7d2988f249d347d5fdae831236. Inference measured with “caffe time –forward_only” command, training measured with “caffe time” command. For “ConvNet” topologies, dummy dataset was used. For other topologies, data was stored on local storage and cached in memory before training. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models (GoogLeNet, AlexNet, and ResNet-50), https://github.com/intel/caffe/tree/master/models/default_vgg_19 (VGG-19), and https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners (ConvNet benchmarks; files were updated to use newer Caffe prototxt format but are functionally equivalent). GCC 4.8.5, MKLML version 2017.0.2.20170110. BVLC-Caffe: https://github.com/BVLC/caffe, Inference & Training measured with “caffe time” command.  For “ConvNet” topologies, dummy dataset was used. For other topologies, data was st ored on local storage and cached in memory before training  BVLC Caffe

    Configuration for training throughput:

    Processor :2 socket Intel(R) Xeon(R) Platinum 8180 CPU @ 2.50GHz / 28 cores HT ON , Turbo ON Total Memory 376.28GB (12slots / 32 GB / 2666 MHz).CentOS Linux-7.3.1611-Core , SSD sda RS3WC080 HDD 744.1GB,sdb RS3WC080 HDD 1.5TB,sdc RS3WC080 HDD 5.5TB , Deep Learning Framework caffe version: f6d01efbe93f70726ea3796a4b89c612365a6341 Topology :alexnet BIOS:SE5C620.86B.00.01.0009.101920170742 MKLDNN: version: ae00102be506ed0fe2099c6557df2aa88ad57ec1 NoDataLayer. Measured: 1023 imgs/sec vs Platform: 2S Intel® Xeon® CPU E5-2699 v3 @ 2.30GHz (18 cores), HT enabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 256GB DDR4-2133 ECC RAM. CentOS Linux release 7.3.1611 (Core), Linux kernel 3.10.0-514.el7.x86_64. OS drive: Seagate* Enterprise ST2000NX0253 2 TB 2.5″ Internal Hard Drive. Performance measured with: Environment variables: KMP_AFFINITY=’granularity=fine, compact,1,0‘, OMP_NUM_THREADS=36, CPU Freq set with cpupower frequency-set -d 2.3G -u 2.3G -g performance. Deep Learning Frameworks: Intel Caffe: (http://github.com/intel/caffe/), revision b0ef3236528a2c7d2988f249d347d5fdae831236. Inference measured with “caffe time –forward_only” command, training measured with “caffe time” command. For “ConvNet” topologies, dummy dataset was used. For other topologies, data was stored on local storage and cached in memory before training. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models (GoogLeNet, AlexNet, and ResNet-50), https://github.com/intel/caffe/tree/master/models/default_vgg_19 (VGG-19), and https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners (ConvNet benchmarks; files were updated to use newer Caffe prototxt format but are functionally equivalent). GCC 4.8.5, MKLML version 2017.0.2.20170110. BVLC-Caffe: https://github.com/BVLC/caffe, Inference & Training measured with “caffe time” command.  For “ConvNet” topologies, dummy dataset was used. For other topologies, data was st ored on local storage and cached in memory before training  BVLC Caffe (http://github.com/BVLC/caffe), revision 91b09280f5233cafc62954c98ce8bc4c204e7475 (commit date 5/14/2017). BLAS: atlas ver. 3.10.1.

    2Intel editorial by Brian Krzanich, chief executive officer of Intel Corporation, “Announcing Industry’s First Neural Network Processor,” October 17, 2017.

    Intel, the Intel logo, Xeon, and Nervana are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries.

    Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. Check with your system manufacturer or retailer or learn more at www.intel.com.



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