Presentation on theme: "Major Sponsors/Supporters"— Presentation transcript:
1 Cloud Computing: Vision, Tools, Technologies for Delivering Computing as the 5th Utility
2 Major Sponsors/Supporters Cloud Computing: Vision, Tools, and Technologies for Delivering Computing as the 5th UtilityDr. Rajkumar BuyyaCloud Computing and Distributed Systems (CLOUDS) Lab Dept. of Computer Science and Software Engineering The University of Melbourne, AustraliaMajor Sponsors/Supporters
3 Outline “Computer Utilities” Cloud Computing and Related Paradigms Vision and Promising IT Paradigms/PlatformsCloud Computing and Related ParadigmsTrends, Definition, Cloud Benefits and ChallengesMarket-Oriented Cloud ArchitectureSLA-oriented Resource AllocationGlobal Cloud ExchangeEmerging Cloud PlatformsCloudbus: Melbourne Cloud Computing ProjectSummary and Thoughts for Future
4 “Computer Utilities” Vision: Implications of the Internet 1969 – Leonard Kleinrock, ARPANET project“As of now, computer networks are still in their infancy, but as they grow up and become sophisticated, we will probably see the spread of ‘computer utilities’, which, like present electric and telephone utilities, will service individual homes and offices across the country”Computers Redefined1984 – John Gage, Sun Microsystems“The network is the computer”2008 – David Patterson, U. C. Berkeley“The data center is the computer. There are dramatic differences between of developing software for millions to use as a service versus distributing software for millions to run their PCs”2008 – “The Cloud is the computer” – Buyya!
6 Outline “Computer Utilities” Cloud Computing and Related Paradigms Vision and Promising IT Paradigms/PlatformsCloud Computing and Related ParadigmsTrends, Definition, Cloud Benefits and ChallengesMarket-Oriented Cloud ArchitectureSLA-oriented Resource AllocationGlobal Cloud ExchangeEmerging Cloud PlatformsCloudbus: Melbourne Cloud Computing ProjectSummary and Thoughts for Future
7 Gold rush: Too many people are “In Search” of Cloud Computing! Legend: Cluster computing, Grid computing, Cloud computing
8 2009 Gartner IT Hype Cycle of Emerging Technologies
9 Defining Clouds: There are many views for what is cloud computing? Over 20 definitions:Buyya’s definition"A Cloud is a type of parallel and distributed system consisting of a collection of inter-connected and virtualised computers that are dynamically provisioned and presented as one or more unified computing resources based on service-level agreements established through negotiation between the service provider and consumers.”Keywords: Virtualisation (VMs), Dynamic Provisioning (negotiation and SLAs), and Web 2.0 access interface
10 Cloud Services Infrastructure as a Service (IaaS) CPU, Storage: Amazon.com, Nirvanix, GoGrid….Platform as a Service (PaaS)Google App Engine, Microsoft Azure, Manjrasoft Aneka..Software as a Service (SaaS)SalesForce.ComSoftware as a Service (SaaS)Platform as a Service (PaaS)Infrastructure as a Service (IaaS)
11 Clouds based on Ownership and Exposure Public/Internet Clouds3rd party, multi-tenant Cloudinfrastructure & services:* available on subscription basis (pay as you go)Private/Enterprise CloudsCloud computing model run within a company’s own Data Center / infrastructure for internal and/or partners use.Hybrid/Mixed CloudsMixed usage of private and public Clouds:Leasing public cloud services when private cloud capacity is insufficient
12 (Promised) Benefits of (Public) Clouds No upfront infrastructure investmentNo procuring hardware, setup, hosting, power, etc..On demand accessLease what you need and when you need..Efficient Resource AllocationGlobally shared infrastructure, can always be kept busy by serving users from different time zones/regions...Nice PricingBased on Usage, QoS, Supply and Demand, Loyalty, …Application AccelerationParallelism for large-scale data analysis, what-if scenarios studies…Highly Availability, Scalable, and Energy EfficientSupports Creation of 3rd Party Services & Seamless offeringBuilds on infrastructure and follows similar Business model as Cloud
14 When will Cloud spending become 50% of IT spending or reach to a several trillion $ business/year? 600?1000?30%50%120?15%20162020?2020?Buyya’s Estimate!
15 Cloud Computing Challenges: Dealing with too many issues Resource MeteringBillingPricingScalabilityReliabilityVirtualizationQoSService LevelAgreementsEnergy EfficiencyProvisioningon DemandUtility & Risk ManagementSecurityPrivacyTrustLegal & RegulatoryUhm, I am not quiteclear…Yet anothercomplex IT paradigm?Software Eng. ComplexityProgramming Env. & Application Dev.
16 Outline “Computer Utilities” Cloud Computing and Related Paradigms Vision and Promising IT Paradigms/PlatformsCloud Computing and Related ParadigmsTrends, Definition, Cloud Benefits and ChallengesMarket-Oriented Cloud ArchitectureSLA-oriented Resource AllocationGlobal Cloud ExchangeEmerging Cloud PlatformsCloudbus: Melbourne Cloud Computing ProjectSummary and Thoughts for Future
17 Realizing the ‘Computer Utilities’ Vision: What Consumers and Providers Want? Consumers – minimize expenses, meet QoSHow do I express QoS requirements to meet my goals?How do I assign valuation to my applications?How do I discover services and map applications to meet QoS needs?How do I manage multiple providers and get my work done?How do I outperform other competing consumers?…Providers – maximise Return On Investment (ROI)How do I decide service pricing models?How do I specify prices?How do I translate prices into resource allocations?How do I assign and enforce resource allocations?How do I advertise and attract consumers?How do I perform accounting and handle payments?Mechanisms, tools, and technologiesvalue expression, translation, and enforcement
18 Market-based Systems = Self-managed and self-regulated systems. ComplexitySupply and DemandEnhance Utility123penalty
19 Market-oriented Cloud Architecture: QoS negotiation and SLA-based Resource Allocation
21 Outline 21st Century Vision of Computing Promising Computing ParadigmsCloud Computing and Related ParadigmsTrends, Definition, Characteristics, ArchitectureMarket-Oriented Cloud ArchitectureSLA-oriented Resource AllocationGlobal Cloud ExchangeEmerging Cloud PlatformsCloudbus: Melbourne Cloud Computing ProjectSummary and Thoughts for Future
22 Some Commercial-Oriented Cloud platforms/technologies SystemPropertyAmazon EC2 & S3Google App EngineMicrosoft AzureManjrasoft AnekaFocusIaaSIaaS/PaaSPaaSService TypeCompute (EC2), Storage (S3)Web appsWeb and non-web appsCompute/DataVirtualisationOS Level: XenApps containerOS level/Hyper-VResource Manager and SchedulerDynamic Negotiation of QoSNoneSLA-oriented/ Resource ReservationUser Access InterfaceEC2 Command-line ToolsWeb-based Administration ConsoleWindows Azure portalWorkbench, ToolsWeb APIsYesValue-added Service ProvidersNoProgramming FrameworkAmazon Machine Image (AMI)Python.NET frameworkMultiple App models in.NET languages
23 Complex decisions to make? Many Cloud Offerings: Good, but new issues-“vendor lock in”, “scaling” across cloudsManjrasoft AnekaAmazon EC2Amazon S3Google AppEngineNirvanixMossoMicrosoft AzureCitrix XenServerIaaSPaaSSaaSVMWareHypervisorsXenHyper-V…Public CloudPrivate CloudHybrid CloudComplex decisions to make?
24 InterCloud: Global Cloud Exchange and Market Maker Storage CloudCompute CloudComputeCloudDirectoryBankAuctioneerGlobal CloudExchangeEnterpriseResourceManager(Proxy)Broker 1Enterprise IT ConsumerPublish OffersRequestCapacityNegotiate/BidBroker N.
25 Outline “Computer Utilities” Cloud Computing and Related Paradigms Vision and Promising IT Paradigms/PlatformsCloud Computing and Related ParadigmsTrends, Definition, Cloud Benefits and ChallengesMarket-Oriented Cloud ArchitectureSLA-oriented Resource AllocationGlobal Cloud ExchangeEmerging Cloud PlatformsCloudbus: Melbourne Cloud Computing ProjectSummary and Thoughts for Future
26 Cloudbus@CLOUDS Lab: Melbourne Cloud Computing Initiative Market-Oriented CloudsSLA-based Resource ManagementGlobal Cloud Exchange Elements: BrokersAneka – .NET-based Cloud ComputingPaaS for Enterprise and Public CloudsScaling Across Clouds (Meta Brokering) – Harnessing Compute resourcesFederation of clouds for application scaling across distributed resources3rd Party Cloud Services (e.g., MetaCDN) – Harnessing Storage resourcesBuilding Content Delivery Networks using different “vendors” Storage CloudsGreen Clouds / Data CentersEnergy Efficiency and QoS Oriented Resource AllocationCloudSim: Toolkit for Simulation of CloudsDesign and evaluation for resource management policies & algorithms
27 Aneka: .NET-based Cloud Computing SDK containing APIs for multiple programming models and toolsRuntime Environment for managing application execution managementSuitable forDevelopment of Enterprise Cloud ApplicationsCloud enabling legacy applicationsPortability for Customer Apps:Enterprise ↔ Public Clouds.NET/Win ↔ Mono/Linux
28 QoS Negotiation in Aneka Meta Negotiation Registry…3. MatchingDBDBDBRegistries2. Publishing, Querying1. PublishingMN MiddelwareGridbus BrokerMN Middelware4. Session EstablishmentAnekaMeta-NegotiationAmadeusWorkflowMeta-NegotiationHandshakingHandshakingAlternate OffersNegotiationStrategyAlternate OffersNegotiationStrategyLocal SLA TemplateWSDLLocal SLA Template5. NegotiationParty 1APIParty 2Service ConsumerService Provider6. Service Invocation
35 User scenario: GoFront (unit of China Southern Railway Group) Application: Locomotive design CAD renderingRaw Locomotive Design Files(Using AutoDesk Maya)Using Maya Graphical Mode DirectlyCase 1: Single Server4 cores serverAneka Maya RendererUse private Aneka CloudGoFront Private Aneka CloudLAN network(Running Maya Batch Mode on demand)Case 2: Aneka Enterprise CloudAneka utilizes idle desktops (30) to decrease task time from days to hoursTime (in hrs)Single ServerAneka Cloud
36 Providing a scalable architecture for TitanStrike on-line Gaming Portal TitanStrike Private Aneka CloudLAN network(Running Game plugins on Demand)Case 2: Aneka Enterprise Cloud = ScalabilityAneka-based GameControllerThe local scheduler interacts with Aneka and distributes the load in the cloud.Distributedlog parsinglogsCase 1: Single Server = Huge OverloadSingle scheduler controlling the execution of all the matches.Game ServersGamers profilesPlayers statisticsTeam playingMultiple gamesTitan Strike On Line Gaming PortalCentralizedlog parsinglogsSingle GameController
37 DNA MicroArray Data Analysis for BRCA (Brain Cancer gene profiles) Aneka onPublic Cloud –Amazon EC2
38 Gene Expression Profiling Classification on Public Clouds Co-XCS Model: multipleCoXCS Classifiers evolveseparately on a differentpartition and exchangeindividuals at predefinedstagesCloud CoXCSDevelopment and executionenvironment of composableworkflows that are run ontop of distributed middlewarevia plug-in based engines.Aneka distribution engine:dispatches CoXCS tasksin the Aneka Cloud.OffspringThe diagram shows the overal architecture of Cloud-CoXCS. The system is composed by three component:The CoXCS model: that is represented by the collection of classifier instances that are running in parallel and evolve on separate partition of the datasetThe Offspring development and runtime environment which enables to quickly compose workflows and manage their execution over different distribution middlewaresAneka Cloud on Amazon EC2 compute cloud as a dsitributed middleware.Aneka deployment on EC2 public Compute Cloud
39 Experiments on Amazon EC2 Master image: Aneka container with scheduling and task model file staging services deployed on Windows Server 2003Worker image: Aneka Container with task execution services deployed on RedHat LinuxExecution time (in minutes)c1.medium
40 Building 3rd Party Cloud Services – Harnessing Storage Clouds Building Next-Gen “Content Delivery Networks”
41 MotivationsContent Delivery Networks (CDNs) such as Akamai place web server clusters in numerous geographical locations – ”huge upfront investment”to improve the responsiveness and locality of the content it hosts for end-users.However, their services are priced out of reach for all but the largest enterprise customers.Hence, we have developed an alternative approach to content delivery by leveraging infrastructure ‘Storage Cloud’ providers at a fraction of the cost of traditional CDN providers – “pay as you go”
44 Meta Brokering – Harnessing Compute Clouds for Application Scaling Extending market-oriented Grid Ideas with Cloud computing
45 Building a Grid of Clouds Global Utility Computing Grid Information ServiceGrid Resource BrokerdatabaseApplicationR2R3R4R5RNGrid Resource BrokerR6R1Resource BrokerGrid Information Service
46 Gridbus Service Broker (GSB) A resource broker for scheduling task farming data-intensive applications with static or dynamic parameter sweeps on global Grids and Clouds.It uses computational economy paradigm for optimal selection of computational and data services depending on their quality, cost, and availability, and users’ QoS requirements (deadline, budget, & T/C optimisation)Key FeaturesA single window to manage & control experimentProgrammable Task Farming EngineResource Discovery and Resource TradingOptimal Data Source DiscoveryScheduling & PredicationsGeneric Dispatcher & Grid AgentsTransportation of data & sharing of resultsAccounting
51 Experiment Setup: DBC Scheduling with Optimize for (1) Time & (2) Cost Workload:A parameter sweep “synthetic” application (100 jobs), each job is modeled to execute ~5 minute with variation of (+/-20 sec.).QoS Constraints: Deadline: 40 min. and Budget: $6Resources:USEuropeAustraliaR*R2Information ServiceR1R4,5Resource Broker
52 Resources & Price (multiplier for clarity) OrganizationResource DetailsRate(Cents per second*1000 )Total JobsTime-OptCost-OptGeorgia State University, USsnowball.cs.gsu.edu8 Intel 1.90GHz CPU, 3.2 GB RAM, 152 GB HD, Linux90 (0.09)3211H. Furtwangen University, Germanyunimelb.informatik.hs-furtwangen.de1 Athlon XP CPU, 767 MB RAM, 147 GB HD345University of California-Irvine, USharbinger.calit2.uci.edu2 Intel P III 930 MHz CPU, 503 MB RAM, 32 GB HD2810University of Melbourne, Australiabillabong.csse.unimelb.edu.au2 Intel(R) 2.40GHz CPU, 1 GB RAM, 35 GB HD6gieseking.csse.unimelb.edu.au2 Intel(R) 2.40GHz CPU, 1 GB RAM, 71 GB HDAmazon EC2 *ec2-Medium instance5 EC2 Compute Units*, 1.7 GB RAM, 350 GB HD6014165 EC2 Compute Units, 1.7 GB RAM, 350 GB HD13ec2-Small instance1 EC2 Compute Unit, 1.7 GB RAM, 160 GB HD307Total Price / Budget Consumed5.04$3.71$Time to Complete Execution28 min35 min* Amazon charges for 1 hour even if you use VM for 1 sec. We should force Amazon to change Charging Policy from 1hr block to actual usage! Or invent a 3rd party service that manages this by leasing smaller slots.
57 Resources Consumed by Cost and Time Opt. Strategies Cost-OptTime-OptUniMelb: .006EC2-m: .06UniMelb: .006EC2-m : .06UCi: .002EC2-s : .03EU: .003EC2-s: .03Georgia: .09(most expensive)QoS Constraints: Deadline: 40 min. and Budget: $6TimeCostBudget Consumed5.04$3.71$Time to Complete28 min35 min
58 Experimental Evaluation is too much of work and “expensive” for computing researchers? CloudSim: Performance Evaluation Made Easy*Repeatable, scalable, controllable environment for modelling and simulation of Clouds* No need to worry about paying IaaS provides + CloudSim is FREE!
59 The CloudSim Toolkit http://www.cloudbus.org/cloudsim/
60 Outline “Computer Utilities” Cloud Computing and Related Paradigms Vision and Promising IT Paradigms/PlatformsCloud Computing and Related ParadigmsTrends, Definition, Cloud Benefits and ChallengesMarket-Oriented Cloud ArchitectureSLA-oriented Resource AllocationGlobal Cloud ExchangeEmerging Cloud PlatformsCloudbus: Melbourne Cloud Computing ProjectSummary and Thoughts for Future
61 SummarySeveral Computing Platforms/Paradigms are promising to deliver “Computing Utilities” visionCloud Computing is the most recent kid in the block promising to turn vision into realityClouds built on: SOA, VMs, Web 2.0 technologiesMany exciting business and consumer applications enabled.Market Oriented Clouds are getting realNeed to move from static pricing to dynamic pricingNeed strong support for SLA-based resource management3rd party Composed Cloud services starting to emergeBuilding Grids using Clouds is much more realistic.Extension of idea can lead to “Global Cloud Exchange”
62 Dozens of Open Research Issues (Application) Software LicensingSeamless integration of private and Cloud resourcesSecurity, Privacy and TrustCloud “Lock-In” worries and InteroperabilityApplication Scalability Across Multiple CloudsClouds Federation and Cooperative SharingGlobal Cloud Exchange and Market MakerDynamic PricingDynamic Negotiation and SLA ManagementEnergy Efficient Resource Allocation and User QoSPower-Cost and CO2 emission issuesUse renewable energy: follow Sun and wind?Regulatory and Legal Issues
63 Convergence of Competing Paradigms/Communities Needed }?WebData CentresUtility ComputingService ComputingGrid ComputingP2P ComputingCloud ComputingMarket-Oriented Computing…+Ubiquitous accessReliabilityScalabilityAutonomicDynamic discoveryComposabilityQoSSLA…Trillion $ businessWho will own it?ParadigmsAttributes/Capabilities
64 References Blueprint Paper! Aneka Documents: The Grid Economy Paper: R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, I. Brandic, “Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility”, Future Generation Computer Systems (FGCS) Journal, June 2009.Aneka Documents:The Grid Economy Paper:R. Buyya, D. Abramson, S. Venugopal, “The Grid Economy”, Proceedings of the IEEE, No. 3, Volume 93, IEEE Press, 2005.MetaCDN Paper:James Broberg, Rajkumar Buyya, and Zahir Tari, MetaCDN: Harnessing 'Storage Clouds' for High Performance Content Delivery, Journal of Network and Computer Applications, ISSN: , Elsevier, Amsterdam, The Netherlands, 2009.Cloudbus Keynote Paper:R. Buyya, S. Pandey, and C. Vecchiola, Cloudbus Toolkit for Market-Oriented Cloud Computing, Proceeding of the 1st International Conference on Cloud Computing (CloudCom 2009, Springer, Germany), Beijing, China, December 1-4, 2009.
65 Thanks for your attention! Are there anyQuestions?Comments/ SuggestionsWe Welcome Cooperation in R&D and Business! ||