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Cloud Computing and Grid PowerPoint Presentation

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  • Slide 1 - CLOUDS
  • Slide 2 - Grid Computing, MIERSI, DCC/FCUP 2 Definition “A large-scale distributed computing paradigm that is driven by economies of scale, in which a pool of abstracted, virtualized, dynamically-scalable, managed computing power, storage, platforms, and services are delivered on demand to external customers over the Internet.” (According to Foster, Zhao, Raicu and Lu, Cloud Computing and Grid Computing 360-Degree Compared, 2008)
  • Slide 3 - Grid Computing, MIERSI, DCC/FCUP 3 Cloud Computing Just a new name for Grid?
  • Slide 4 - Grid Computing, MIERSI, DCC/FCUP 4 Cloud Computing Just a new name for Grid? Yes…
  • Slide 5 - Grid Computing, MIERSI, DCC/FCUP 5 Cloud Computing Just a new name for Grid? …No….
  • Slide 6 - Grid Computing, MIERSI, DCC/FCUP 6 Cloud Computing Just a new name for Grid? Nevertheless Yes!!!
  • Slide 7 - Grid Computing, MIERSI, DCC/FCUP 7 Cloud: just a new name for Grid? YES: Reduce the cost of computing Increase reliability Increase flexibility (third party)
  • Slide 8 - Grid Computing, MIERSI, DCC/FCUP 8 Cloud: just a new name for Grid? NO: Great increase demand for computing (clusters, high speed networks) Billions of dollars being spent by Amazon, Google, Microsoft to create real commercial large-scale systems with hundreds of thousands of computers – www.top500.org shows computers with 100,000+ cores Analysis of massive data
  • Slide 9 - Grid Computing, MIERSI, DCC/FCUP 9 Cloud: just a new name for Grid? Nevertheless YES: Problems are the same in clouds and grids Common need to manage large facilities Define methods to discover, request and use resources Implement highly parallel computations
  • Slide 10 - Grid Computing, MIERSI, DCC/FCUP 10 Clouds: key points of the definition Differences related to traditional distributed paradigms: Massively scalable Can be encapsulated as an abstract entity that delivers different levels of service Driven by economies of scale Services can be dynamically configured (via virtualization or other approaches) and delivered on demand
  • Slide 11 - Grid Computing, MIERSI, DCC/FCUP 11 Clouds: reasons for interest Rapid decrease in hw cost, increase in computing power and storage capacity (multi-cores etc) Exponentially growing data size Widespread adoption of Services Computing and Web 2.0 apps
  • Slide 12 - Grid Computing, MIERSI, DCC/FCUP 12 Clouds: relation with other paradigms
  • Slide 13 - Grid Computing, MIERSI, DCC/FCUP 13 Clouds: yet about definition… “The interesting thing about Cloud Computing is that we’ve redefined Cloud Computing to include everything that we already do. . . . I don’t understand what we would do differently in the light of Cloud Computing other than change the wording of some of our ads.” Larry Ellison (Oracle CEO), quoted in the Wall Street Journal, September 26, 2008
  • Slide 14 - Grid Computing, MIERSI, DCC/FCUP 14 Clouds: yet about definition… “A lot of people are jumping on the [cloud] bandwagon, but I have not heard two people say the same thing about it. There are multiple definitions out there of “the cloud.”” Andy Isherwood (HP VP of sales), quoted in ZDnet News, December 11, 2008
  • Slide 15 - Grid Computing, MIERSI, DCC/FCUP 15 Clouds: yet about definition… “It’s stupidity. It’s worse than stupidity: it’s a marketing hype campaign. Somebody is saying this is inevitable — and whenever you hear somebody saying that, it’s very likely to be a set of businesses campaigning to make it true.” Richard Stallman (known for his advocacy of free software), quoted in The Guardian, September 29, 2008
  • Slide 16 - Grid Computing, MIERSI, DCC/FCUP 16 Clouds: yet about definition… From a hardware point of view, three aspects are new in Cloud Computing: The illusion of infinite computing resources available on demand, thereby eliminating the need for Cloud Computing users to plan far ahead for provisioning; The elimination of an up-front commitment by Cloud users, thereby allowing companies to start small and increase hardware resources only when there is an increase in their needs; and The ability to pay for use of computing resources on a short-term basis as needed (e.g., processors by the hour and storage by the day) and release them as needed, thereby rewarding conservation by letting machines and storage go when they are no longer useful.
  • Slide 17 - Grid Computing, MIERSI, DCC/FCUP 17 Clouds: side-by-side comparison with grids Business model Architecture Resource Management Programming model Application model Security model
  • Slide 18 - Grid Computing, MIERSI, DCC/FCUP 18 Clouds: side-by-side comparison with grids Business model Traditional: one-time payment for unlimited use of software Clouds: pay the provider on a comsumption basis, computing and storage (like electricity, gas etc) Grids: project-oriented, trading, negotiation, provisioning, and allocation of resources based on the level of services provided
  • Slide 19 - Grid Computing, MIERSI, DCC/FCUP 19 Clouds: side-by-side comparison with grids Architecture Grid Protocol Architecture
  • Slide 20 - Grid Computing, MIERSI, DCC/FCUP 20 Clouds: side-by-side comparison with grids Fabric Layer: same as grid fabric layer (resources) Unified Resource Layer: resources that have been abstracted/encapsulated (usually by virtualization) – virtual computer or cluster, logical file system,, database etc. Platform Layer: web hosting environment, scheduling service etc.
  • Slide 21 - Grid Computing, MIERSI, DCC/FCUP 21 Clouds: side-by-side comparison with grids It is possible for clouds to be implemented over existing grid technologies leveraging more than a decade of community efforts on standardization, security, resource management, and virtualization support!
  • Slide 22 - Grid Computing, MIERSI, DCC/FCUP 22 Clouds: services Infrastructure as a Service (IaaS): hw, sw, equipments, can scale up and down dynamicallly (elastic). E.g.: Amazon Elastic Compute Cloud (EC2) and Simple Storage Service (S3) Eucalyptus: open source Cloud implementation compatible with EC2 (allows to set up local cloud infra prior to buying services)
  • Slide 23 - Grid Computing, MIERSI, DCC/FCUP 23 Clouds: services Platform as a Service (PaaS): offers high level integrated environment to build, test, and deploy custom apps. Restrictions on sw used to develop apps in exchange for built-in scalability. E.g.: Google App Engine
  • Slide 24 - Grid Computing, MIERSI, DCC/FCUP 24 Clouds: services Software as a Service (SaaS): delivers special purpose software that is remotely accessible. E.g,: Google Maps, Live Mesh from Microsoft etc
  • Slide 25 - Grid Computing, MIERSI, DCC/FCUP 25 Clouds: side-by-side comparison with grids Resource management Compute model Data model Virtualization Monitoring provenance
  • Slide 26 - Grid Computing, MIERSI, DCC/FCUP 26 Clouds: side-by-side comparison with grids Resource management Compute model Grids: batch-scheduled (queueing systems) Clouds: resources shared by all users at the same time (??!) in contrast to dedicated resources in queueing systems Maybe one of the major challenges in clouds: QoS!
  • Slide 27 - Grid Computing, MIERSI, DCC/FCUP 27 Clouds: side-by-side comparison with grids Resource management Multiple virtual machines can share CPUs and main memory well, but…. Network and disk I/O sharing is problematic
  • Slide 28 - Grid Computing, MIERSI, DCC/FCUP 28 Clouds: side-by-side comparison with grids Resource management 75 EC2 instances running STREAM (memory benchmark) Mean bw = 1355 MB/s +- 52 MB/s (~4%) Avg disk bw (to write 1GB) 55 MB/s +- 9MB (16%) I/O interference needs to be solved! Back to the architecture of mainframes??? Use of flash memory (faster access)?
  • Slide 29 - Grid Computing, MIERSI, DCC/FCUP 29 Clouds: side-by-side comparison with grids Resource management Data model: Centralized on Cloud computing? Future trend according to Foster, Zhao, Raicu and Lu:
  • Slide 30 - Grid Computing, MIERSI, DCC/FCUP 30 Clouds: side-by-side comparison with grids Resource management Data model: Grids: concept of virtual data, replica, metadata catalog, abstract structural representation Data locality: to achieve good scalability data must be distributed over many computers Clouds: use map-reduce mechanism like in Google to maintain data locality Grids: rely on shared file systems (NFS, GPFS, PVFS, Lustre)
  • Slide 31 - Grid Computing, MIERSI, DCC/FCUP 31 Clouds: side-by-side comparison with grids Resource management Combining compute and data model: Important to schedule computational tasks close to their data! Another challenge for clouds since data-intensive apps are currently not the typical apps running in cloud environments Currently data-intensive apps have been attracting the interest of many companies
  • Slide 32 - Grid Computing, MIERSI, DCC/FCUP 32 Clouds: side-by-side comparison with grids Resource management Virtualization: Abstraction and encapsulation Clouds: rely heavily on virtualization Grids: do not rely on virtualization as much as clouds. One example of use in Grids: Nimbus (previous Virtual Workspace Service)
  • Slide 33 - Grid Computing, MIERSI, DCC/FCUP 33 Clouds: side-by-side comparison with grids Resource management Cloud Virtualization: Server and app consolidation (multiple apps can run on the same server, resources can be utilized more efficiently) Configurability App availabillity (recovery) Improved responsiveness Meet SLA requirements AMD and Intel have been introducing hw support for virtualization  more efficiency
  • Slide 34 - Grid Computing, MIERSI, DCC/FCUP 34 Clouds: side-by-side comparison with grids Resource management Monitoring: Clouds: hard to do fine-control because of virtualization (problem for users and admins). In the future maybe not a problem as clouds become self-maintained and self-healing (autonomic) Grids: several tools for monitoring (e.g. Ganglia)
  • Slide 35 - Grid Computing, MIERSI, DCC/FCUP 35 Clouds: side-by-side comparison with grids Resource management Provenance: Grids: built into a workflow system to support discovery and reproducibility of scientific results (Chimera, Swift, Kepler, VIEW etc) Clouds: still unexplored Scalable provenance querying and secure access to provenance info are still open problems for both grids and clouds
  • Slide 36 - Grid Computing, MIERSI, DCC/FCUP 36 Clouds: side-by-side comparison with grids Programming model Grids: heavy use of workflow tools to be able to manage large sets of tasks and data. Focus on management rather than on interprocess communication, others: MPICH-G2, WSRF, GridRPC… Clouds: most use the map-reduce programming model. Implementation: Hadoop that uses Pig as a declarative programming language
  • Slide 37 - MapReduce: “Hello World”: Word Count Map(String docid, String text): for each word w in text: Emit(w, 1); Reduce(String term, Iterator values): int sum = 0; for each v in values: sum += v; Emit(term, value); Grid Computing, MIERSI, DCC/FCUP 37
  • Slide 38 - Grid Computing, MIERSI, DCC/FCUP 38 Clouds: side-by-side comparison with grids Programming model Clouds: Microsoft uses Cosmos (distributed storage system) and Dryad processing framework. DryadLINQ and Scope: declarative programming models Others: scripting languages: JavaScript, PHP, Python etc) Google App Engine uses Python as scripting language and GQL to query the BigTable storage system Interoperability: main challenge!
  • Slide 39 - Grid Computing, MIERSI, DCC/FCUP 39 Clouds: side-by-side comparison with grids Application model Clouds: because of the use of virtualization may have difficulties in successfully running HPC applications that need fast and low latency networks Both grids and clouds have the capability to run any kind of application
  • Slide 40 - Grid Computing, MIERSI, DCC/FCUP 40 Clouds: side-by-side comparison with grids Security model Clouds: seem to have a relatively simpler and less secure model than in grids, but virtualization gives a level of security Grids impose a stricter security model
  • Slide 41 - Grid Computing, MIERSI, DCC/FCUP 41 Clouds: side-by-side comparison with grids Security model a user should raise the risks with vendors: Privileged user access Regulatory compliance Data location Data segregation Recovery Investigative support Long-term viability
  • Slide 42 - Grid Computing, MIERSI, DCC/FCUP 42 Concluding… Still much to do…. Ideal: centralized scale of today´s Cloud utilities and the distribution and interoperability of today´s Grid facilities
  • Slide 43 - Grid Computing, MIERSI, DCC/FCUP 43 Concluding… This topic is not for you… If you’re not genuinely interested in the topic If you’re not ready to do a lot of programming If you’re not open to thinking about computing in new ways If you can’t cope with uncertainly, unpredictability, poor documentation, and immature software If you can’t put in the time Otherwise, working in these areas can be richly rewarding! Quoted from Jimmy Lin, Maryland
  • Slide 44 - Grid Computing, MIERSI, DCC/FCUP 44 Relevant links http://cloud-standards.org/wiki/index.php?title=Main_Page Blog of Krishna Sankar: http://doubleclix.wordpress.com/2009/02/14/a-berkeley-view-of-cloud-computing-an-analysis-the-good-the-bad-and-the-ugly/
  • Slide 45 - Grid Computing, MIERSI, DCC/FCUP 45 Papers Above the Clouds: a Berkeley view of Cloud Computing (Feb 2009) Cloud Computing and Grid Computing 360-degree compared (2008) Virtual Workspace Service/Nimbus: Contextualization: Providing one-click virtual clusters Initiatives: EC2 (Amazon), Azure (Microsoft), PoolParty, Cloud9, Eucalyptus….
  • Slide 46 - Grid Computing, MIERSI, DCC/FCUP 46 Available to try Eucalyptus PoolParty ElasticHosts EC2/S3 Cloud9 ….

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