The Gridbus Middleware: Creating and Managing Utility Grids for Powering e-Science and e-Business Applications Dr. Rajkumar Buyya Grid Computing and Distributed Systems (GRIDS) Laboratory Dept. of Computer Science and Software Engineering The University of Melbourne, Australia ww.gridbus.org ww.gridbus.org Gridbus Sponsors

2 Outline Introduction to the University Melbourne, GRIDS Lab, and Opportunities Recap of the First Lecture What are Grids, Challenges, Middleware Solutions Service-Oriented Grid Architecture and Gridbus Solutions Market-based Management, Grid Market Directory, Grid Bank Grid Service Broker Architecture, Design and Implementation Performance Evaluation: Experiments in Creation and Deployment of Applications on Global Grids A Case Study in High Energy Physics Summary and Conclusion

3  Melbourne is Australia’s 2 nd largest and the most multicultural city.  Melbourne’s residents represent 110 nationalities and speak 151 languages  Greater Melbourne population is 3.5 million  Extensive parks and gardens, restaurants, cafes, bustling market places, theatre, art and entertainment  Outstanding Educational and Research Institutes  …  Often rated as World’s most livable city Melbourne! How does it compares to Osaka city?

4  Established 1853  One of the oldest universities in Australia and the Asia Pacific region.  A comprehensive university (Arts, Science, Engineering, Economics, Business, Agriculture, Medicine…)  Engineering started in  Currently a total of 43,000 students  30% of them are International. University of Melbourne at a Glance

5  Ranked as Australia’s #1 University in Research.  Ranked #19 in Top200 Universities Worldwide by Times Higher Education.  UoM is the only Australian University in TOP 20 in the world.  Engineering and ICT #18 in the world.  Strong in Biomedical Research - #10 in the world.  Internationally renowned academics who are leading researchers in their respective fields including 4 Nobel Laureates Global Standing

6 GRIDS Melbourne Youngest and one of the rapidly growing research labs in our School/University: Founded in 2002 Houses: Research Fellows (3) Research Programmers (3) PhD candidates (10) Honours/Masters students (5+) Funding National and International organizations Australian Research Council Many industries (Sun, StorageTek, Microsoft, IBM, Microsoft) University-wide collaboration: Faculties of Science, Engineering, and Medicine Many national and international collaborations. Academics Industries Software: Widely in academic and industrial users. Publication: My research team over 20% of our Dept’s research output. EducationR & D + Community Services: e.g., IEEE TC for Scalable Computing

7 GRIDS Lab Research Probes Grid Economy and Scheduling Data Grid Brokering and Scheduling Workflow Scheduling and Grid Economy Cluster Economy and Scheduling.NET Based Grid Computing Grid Market Directory and Service Publication Grid Simulation (GridSim) Resource Usage Accounting Meta Search Engine and Web Services Web-based Grid Portals Gridscape: Grid Monitoring Portal and its Integration with Google Maps P2P Compute Power Market Distributed Application Composition Sensor Grids and Open Sensor Web Architecture

8 GRIDS Lab Books and Publication

9 GRIDS Lab Team Members Origin Australia India China Singapore Indonesia Czech Republic Brazil Bangladesh Korea Japan Many short term visiting researchers from USA and Europe

10 Join us and experience Melbourne Advantage: multicultural environment “World’s most liveable city” The University: Australia’s #1 University Many international students and professors One of the best places in the world to conduct Grid computing research! Melbourne Welcome you.

11 Outline Introduction to the University Melbourne, GRIDS Lab, and Opportunities Recap of the First Lecture What are Grids, Challenges, Middleware Solutions Service-Oriented Grid Architecture and Gridbus Solutions Market-based Management, Grid Market Directory, Grid Bank Grid Service Broker Architecture, Design and Implementation Performance Evaluation: Experiments in Creation and Deployment of Applications on Global Grids A Case Study in High Energy Physics Summary and Conclusion

12 Computer Systems: Single -> Global Computer Systems Distributed SystemsSingle System PC/WorkstationSMP/NUMAVectorMainframe Client ServerClustersGridsPeer-to-Peer (multiple systems) Centralised Decentralised Control and Management

13 What is Grid? (there are several academic definitions, here is ours) A type of parallel and distributed system that enables the sharing, exchange, selection, & aggregation of geographically distributed “autonomous” resources: Computers – PCs, workstations, clusters, supercomputers, laptops, notebooks, mobile devices, PDA, etc; Software – e.g., ASPs renting expensive special purpose applications on demand; Catalogued data and databases – e.g. transparent access to human genome database; Special devices/instruments – e.g., radio telescope – searching for life in galaxy. People/collaborators. depending on their availability, capability, cost, and user QoS requirements. Wide area

14 How Are Grids Used? High-performance computing Collaborative data-sharing Collaborative design Drug discovery Financial modeling Data center automation High-energy physics Life sciences E-Business E-Science Natural language processing & Data Mining Utility computing

15 Grid Challenges Security Resource Allocation & Scheduling Data locality Network Management System Management Resource Discovery Uniform Access Computational Economy Application Construction

16 Open-Source Grid Middleware Projects

17 Layers of Grid Architecture & Middleware Grid resources Desktops, servers, clusters, networks, applications, storage, devices + resource manager + monitor Security Services Authentication, Single sign-on, secure communication Job submission, info services, Storage access, Trading, Accounting, License Resource management and scheduling Grid programming environment and tools Languages, API, libraries, compilers, parallelization tools Grid applications Web Portals, Applications, System level User level Adaptive Management Core Middleware User-Level Middleware

18 The Gridbus Melbourne: Enable Leasing of ICT Services on Demand WWG Pushes Grid computing into mainstream computing Gridbus

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20 Outline Introduction to the University Melbourne, GRIDS Lab, and Opportunities Recap of the First Lecture What are Grids, Challenges, Middleware Solutions Service-Oriented Grid Architecture and Gridbus Solutions Market-based Management, Grid Market Directory, Grid Bank Grid Service Broker Architecture, Design and Implementation Performance Evaluation: Experiments in Creation and Deployment of Applications on Global Grids A Case Study in High Energy Physics Summary and Conclusion

21 What does Grid players want? Grid Consumers Execute jobs for solving varying problem size and complexity Benefit by utilizing distributed resources wisely Tradeoff timeframe and cost Strategy: minimise expenses Grid Providers Contribute resources for executing consumer jobs Benefit by maximizing resource utilisation Tradeoff local requirements & market opportunity Strategy: maximise return on investment

22 What does Grid players require? They need tools and technologies that help them in value expression, value translation, and value enforcement. Grid Service Consumers (GSCs): How do I express QoS requirements ? How do I trade between timeframe & cost ? How do I map jobs to resources to meet my QoS needs? How do I manage Grid dynamics and get my work done? … Grid Service Providers (GSPs) How do I decide service pricing models ? How do I specify them ? How do I translate them into resource allocations ? How do I enforce them ? How do I advertise & attract consumers ? How do I do accounting and handle payments? …

23 Solution 1: Market-Oriented Grid Computing - (a) Sustained Resourced Sharing and (b) Effective Management of Shared Resources Grid Economy

24 Solution 2: Service Oriented Architecture (SOA) A SOA is a contractual architecture for offering and consuming software as services. There are four entities that make up an SOA service provider, service registry, and service consumer (also known as service requestor). The functions or tasks that the service provider offers, along with other functional and technical information required for consumption, are defined in the service definition or contract. provider registry consumer contract

25 Grid Node N Service-Oriented Grid Architecture Grid Servuce Consumer Programming Environments Grid Resource Broker Grid Service Providers Grid Explorer Schedule Advisor Trade Manager Job Control Agent Deployment Agent Trade Server Resource Allocation Resource Reservation R1R1 Misc. services Information Service R2R2 RmRm … Pricing Algorithms Accounting Grid Node1 … Core Middleware Services … … Health Monitor Grid Market Services JobExec Info ? Secure Trading QoS Storage Sign-on Grid Bank Applications Data Catalogue

26 Gridbus and Complementary Technologies – realizing Utility Grid AIX Solaris WindowsLinux.NET Grid Fabric Software Grid Applications Core Grid Middleware User-Level Middleware Grid Bank Grid Exchange & Federation JVM Grid Brokers: X-Parameter Sweep Lang. Gridbus Data Broker MPI CondorSGETomcatPBS Alchemi Workflow IRIXOSF1 Mac Libra GlobusUnicore … … Grid Market Directory PDBCDB Worldwide Grid Grid Fabric Hardware … … PortalsScienceCommerceEngineering … … Collaboratories … … Workflow Engine Grid Storage Economy Grid Economy NorduGridXGrid ExcellGrid Nimrod-G Gridscape

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28 Grid Market Directory (GMD)

29 Grid Bank: Market-based Grid Access Management Grid Resource Broker (GRB) GridBank Payment Module Grid Trade Server GridBank Charging Module GridBank Server Establish Service Costs ApplicationsApplications Grid AgentGrid Resource Meter GridCheque Deploy Agent and Submt Jobs Usage Agreement Resource Usage GridCheque Grid Service Provider (GSP) GridCheque + Resource Usage (GSC Account Charge Grid Service Consumer (GSC) R1R2 R3 R4 User

30 Outline Introduction to the University Melbourne, GRIDS Lab, and Opportunities Recap of the First Lecture What are Grids, Challenges, Middleware Solutions Service-Oriented Grid Architecture and Gridbus Solutions Market-based Management, Grid Market Directory, Grid Bank Grid Service Broker Architecture, Design and Implementation Performance Evaluation: Experiments in Creation and Deployment of Applications on Global Grids A Case Study in High Energy Physics Summary and Conclusion

31 A resource broker for scheduling task farming data Grid applications with static or dynamic parameter sweeps on global Grids. 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 Features A single window to manage & control experiment Programmable Task Farming Engine Resource Discovery and Resource Trading Optimal Data Source Discovery Scheduling & Predications Generic Dispatcher & Grid Agents Transportation of data & sharing of results Accounting Grid Service Broker (GSB)

32 Gridbus Broker Architecture Grid Middleware Gridbus Client Gribus Client Grid Info Server Schedule Advisor Trading Manager Gridbus Farming Engine Record Keeper Grid Explorer GE GIS, NWS TM TS RM & TS Grid Dispatcher RM: Local Resource Manager, TS: Trade Server G G C U Globus enabled node. A L Alchemi enabled node. (Data Grid Scheduler) Data Catalog Data Node Unicore enabled node. $ $ $ App, T, $, Opt (Bag of Tasks Applications)

33 Gridbus Broker and Remote Service Access Enablers Alchemi Gateway UnicoreData Store Access Technology Grid FTP SRB -PBS -Condor -SGE Globus Job manager fork()batch() Gridbus agent Data Catalog -PBS -Condor -SGE -XGrid SSH fork() batch() Gridbus agent Credential Repository MyProxy Home Node/Portal Gridbus Broker fork() batch() -PBS -Condor -SGE -Alchemi -XGrid Portlets

34 Gridbus Services for eScience applications Application Development Environment: XML-based language for composition of task farming (legacy) applications as parameter sweep applications. Task Farming APIs for new applications. Web APIs (e.g., Portlets) for Grid portal development. Threads-based Programming Interface Workflow interface and Gridbus-enabled workflow engine. Resource Allocation and Scheduling Dynamic discovery of optional computational and data nodes that meet user QoS requirements. Hide L ow-Level Grid Middleware interfaces Globus (v2, v4), SRB, Alchemi, Unicore, and ssh-based access to local/remote resources managed by XGrid, Condor, SGE.

35 Figure 3 : Logging into the portal. Drug Design Made Easy! Click Here for Demo

36 Excel Plugin to Access Gridbus Services Excel ExcelGrid Add-In ExcelGrid Runner ExcelGridJob ExcelGrid MiddlewareGridbus BrokerEnterprise Grid 210 0

37 Discover Resources Distribute Jobs Establish Rates Meet requirements ? Remaining Jobs, Deadline, & Budget ? Evaluate & Reschedule Discover More Resources Compose & Schedule Adaptive Scheduling Steps

38 Deadline (D) and Budget (B) Constrained Scheduling Algorithms AlgorithmExecution Time (D) Execution Cost (B) Compute Grid Data Grid Cost OptLimited by DMinimize Yes Cost-Time OptMinimize if possible Minimize Yes Time OptMinimizeLimited by B Yes Conservative- Time Opt MinimizeLimited by B, jobs have guaranteed minimum budget Yes

39 Outline Introduction to the University Melbourne, GRIDS Lab, and Opportunities Recap of the First Lecture What are Grids, Challenges, Middleware Solutions Service-Oriented Grid Architecture and Gridbus Solutions Market-based Management, Grid Market Directory, Grid Bank Grid Service Broker Architecture, Design and Implementation Performance Evaluation: Experiments in Creation and Deployment of Applications on Global Grids A Case Study in High Energy Physics Summary and Conclusion

40 Case Study: High Energy Physics and Data Grid The Belle Experiment KEK B-Factory, Japan Investigating fundamental violation of symmetry in nature (Charge Parity) which may help explain “why do we have more antimatter in the universe?”. Collaboration 1000 people, 50 institutes 100’s TB data currently

41 Case Study: Event Simulation and Analysis B0->D*+D*-Ks Simulation and Analysis Package - Belle Analysis Software Framework (BASF) Experiment in 2 parts – Generation of Simulated Data and Analysis of the distributed data  Analyzed 100 data files (30MB each) that were distributed among the five nodes within Australian Belle DataGrid platform.

42 Australian Belle Data Grid Testbed VPAC Melbourne

43 Belle Data Grid (GSP CPU Service Price: G$/sec) NA G$4 Data node G$6 VPAC Melbourne G$2

44 Belle Data Grid (Bandwidth Price: G$/MB) NA G$4 Data node G$6 VPAC Melbourne G$

45 Deploying Application Scenario A data grid scenario with 100 jobs and each accessing remote data of ~30MB Deadline: 3hrs. Budget: G$ 60K Scheduling Optimisation Scenario: Minimise Time Minimise Cost Results:

46 Time Minimization in Data Grids Time (in mins.) Number of jobs completed fleagle.ph.unimelb.edu.aubelle.anu.edu.aubelle.physics.usyd.edu.aubrecca-2.vpac.org

47 Results : Cost Minimization in Data Grids Time(in mins.) Number of jobs completed fleagle.ph.unimelb.edu.aubelle.anu.edu.aubelle.physics.usyd.edu.aubrecca-2.vpac.org

48 Observation Organization Node detailsCost (in G$/CPU-sec)Total Jobs Executed TimeCost CS,UniMelbbelle.cs.mu.oz.au 4 CPU, 2GB RAM, 40 GB HD, Linux N.A. (Not used as a compute resource) -- Physics, UniMelbfleagle.ph.unimelb.edu.au 1 CPU, 512 MB RAM, 40 GB HD, Linux CS, University of Adelaide belle.cs.adelaide.edu.au 4 CPU (only 1 available), 2GB RAM, 40 GB HD, Linux N.A. (Not used as a compute resource) -- ANU, Canberrabelle.anu.edu.au 4 CPU, 2GB RAM, 40 GB HD, Linux 42 2 Dept of Physics, USyd belle.physics.usyd.edu.au 4 CPU (only 1 available), 2GB RAM, 40 GB HD, Linux VPAC, Melbournebrecca-2.vpac.org 180 node cluster (only head node used), Linux 623 2

49 Outline Introduction to the University Melbourne, GRIDS Lab, and Opportunities Recap of the First Lecture What are Grids, Challenges, Middleware Solutions Service-Oriented Grid Architecture and Gridbus Solutions Market-based Management, Grid Market Directory, Grid Bank Grid Service Broker Architecture, Design and Implementation Performance Evaluation: Experiments in Creation and Deployment of Applications on Global Grids A Case Study in High Energy Physics Summary and Conclusion

50 The GridSim Toolkit A Java based tool for Grid Scheduling Simulations Basic Discrete Event Simulation Infrastructure Virtual Machine (Java, cJVM, RMI) PCs Clusters Workstations... SMPs Distributed Resources GridSim Toolkit Application Modeling Information Services Resource Allocation Grid Resource Brokers or Schedulers’s Simulation Statistics Resource Modeling and Simulation (with Time and Space shared schedulers) Job Management ClustersSingle CPUReservationSMPsLoad Pattern Application Configuration Resource Configuration Visual Modeler Grid Scenario Network SimJavaDistributed SimJava Resource Entities Output Application, User, Grid Scenario’s Input and Results Add your own policy for resource allocation

51 Selected GridSim Users

52 Summary and Conclusion Grids exploit synergies that result from cooperation of autonomous entities: Resource sharing, dynamic provisioning, and aggregation at global level  Great Science and Great Business! Grids have emerged as enabler for Cyberinfrastructure that powers e-Science and e-Business applications. SOA + Market-based Grid Management = Utility Grids Grids allow users to dynamically lease Grid services at runtime based on their quality, cost, availability, and users QoS requirements. Delivering ICT services as computing utilities. Grids offer enormous opportunities for realizing e-Science and e-Business at global level. Use our Gridbus technology to realise this and make money!

53 Melbourne welcomes you Melbourne as a place: multicultural environment “World’s most liveable city” The University: Australia’s #1 University Many international students and professors One of the best places in the world to conduct Grid computing research! My Contact Details:

54 You could be a co-author of my next book!

55 Thanks for your attention! We Welcome Cooperation in Research and Commercialisation! |

56 Big Question? “Can computational grids drive the economy of the 21st century similar to the electric power grid that drove the economy of the 20th century?”

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