The Gridbus Toolkit for Service-Oriented Grid and Utility Computing

1. WW Grid The Gridbus Toolkit for Service-Oriented Grid and Utility Computing Chair in Grid Computing Grid Computing and Distributed Systems (GRIDS) Lab. Dept. of Computer Science and Software EngineeringThe University of Melbourne, Australiawww.gridbus.org Rajkumar Buyya

2. Acknowledgements: Co-authors, Collaborators, and Funding Sources • Gridbus Project Members • Rajkumar Buyya (Gridbus PI) • Srikumar Venugopal (Ph.D. student) • Jia Yu (Ph.D. student) • Anthony Sulistio (Ph.D. student) • Chee Shin Yeo (Ph.D. student) • Manjuka Soysa (Ph.D. student) • Shoaib Burq (Research Assistant) • Martin Placek (B.E student) • Rajiv Ranjan (Masters by Research) • Alex Barmouta (from UWA probing GridBank ) • Ding Choon-Hoong • Akshay Luther (Alchemi .NET Grid framework) • Nimrod-G • David Abramson & Jon Giddy Monash University • Virtual Lab - Docking • Kim Branson, WEHI for Structural Biology • NeuroGrid • Susumu Date, Osaka University • HEPGrid • Lyle Winton, School of Physics • Natural Language Engineering: • Baden Hughes • Finance/Portfolio/investment risk analysis • Rafael Moreno-Vozmediano, Complutense University of Madrid • Industry Collaborators • Charles Milligan, StorageTek • Wolfgang Gentzsch, Sun Microsystems • Benjamin Khoo, IBM Global Services, Singapore • Lawrence Liew, Singapore Computer Systems (SCS) • Collaborators • Manzur Murshed, Monash (GridSim core 1.0) • Heinz Stokinger, CERN (grid economy models) • Jahanzeb Sherwani, Nosheen Ali, Nausheen Lotia, Zahra Hayat (LUMS) • Hussein Gibbins, X – RA, GRIDS lab, Melborune

3. Presentation Outline • Introduction • Grid Challenges, Approaches, and Architecture • Service-Oriented Grid Architecture for Computational Economy • Grid Broker and Scheduling • Nimrod-G and Gridbus Technologies • Deploying Applications on World-Wide Grid • Conclusion

4. Prominent Grid Drivers: Emerging eScinece and eBusiness Apps • Next generation experiments, simulations, sensors, satellites, even people and businesses are creating a flood of data. They all involve numerous experts/resources from multiple organization in synthesis, modeling, simulation,analysis, and interpretation. ~PBytes/sec High Energy Physics Brain Activity Analysis Newswire & data mining: Natural language engineering Digital Biology Life Sciences Astronomy Quantum Chemistry Finance: Portfolio analysis Internet & Ecommerce

5. Analysis Results Analysis Results Online Medical Instrumentation and Neuroscience: A Case for eScience DV transfer Osaka Univ. • Virtual Laboratory • for medicine and brain science • Knowledge sharing • MEG sharing? • Data Sharing Data Generation Osaka Univ. Hospital Data Analysis Life-electronics laboratory, AIST Cybermedia Center • Provision of MEG • Provision of expertise in • the analysis of brain function

6. Traditional (e.g., Oracle 9i) Tight/Vertical Integration of Storage, Database, Application Hosting Server, and Application Elements They reside on a single computing resource. Enhancing capability means a new investment: Replace a machine by new one or upgrade it. Can’t leverage existing resources. Expensive approach. Grid Based (e.g., Oracle 10g) Disintegration of Storage, Database, Application Hosting Server, and Application Elements They reside on a different resources in a Grid environment. Enhancing capability means: Leveraging existing resources Dynamic provisioning Cost-effective approach Enterprise Computing Applications

7. Common Attributes and Requirements of eScience & eBusiness Applications • They involve Distributed Entities: • Participants/Organizations • Resources • Computers • Instruments • Datasets/Databases • Source (e.g., CDB/PDBs) • Replication (e.g, HEP Data) • Application Components • Heterogeneous in nature • Participants require share analysis results of analysis with other collaborators (e.g., HEP) • Grid computing paradigm has emerged as a most promising enabler for eScience applications.

8. database A Bird Eye View of World-Wide Grid Environment Grid Information Service Grid Resource Broker Application R2 R3 R4 R5 RN Grid Resource Broker R6 R1 Resource Broker Grid Information Service

9. What do Grids aim for and how to nurture them? • Grids aim at exploiting synergies that result from cooperation of autonomous distributed entities. Synergies include: • Resource sharing, exchange, and dynamic aggregation. • “On-demand” creation of Virtual Organisations and Enterprises (VOs and VEs) • For this cooperation to be sustainable, Grid service providers needs to have (economic) incentive. • Therefore, “incentive” mechanisms should be considered as one of key design parameters of Grid computing.

10. Synergies that Result from Cooperation:Driver for classes of Grid Applications • Distributed High-Performance Computing (Supercomputing): • Computational science. • High-Capacity/Throughput Computing: • Large scale simulation/chip design & parameter studies. • Content Sharing (free or paid) • Sharing digital contents among peers (e.g., Napster) • Remote software access/renting services: • Application service provides (ASPs) & Web services. • Data-intensive computing: • Drug Design, Particle Physics, Stock Prediction... • On-demand computing: • Dynamic provisioning of resources in enterprise computing. • Collaborative Computing: • Collaborative design, Data exploration, education. • Utility Computing or Service Oriented Computing (SOC): • Towards a market-based Grid computing: Leasing and delivering Grid services as ICT utilities.

11. Grid Challenges and Approaches

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

13. GOC Grid Exchange Grid Operations Management Challenges –dynamic resources, policies, and self interested entities GSP GSP GSP5 GSP1 Grid Economy Technologies GSP4 GSP2 GSP3

14. Australia Nimrod-G Gridbus GridSim Virtual Lab DISCWorld GrangeNet. ..etc Europe UK eScience EU Data Grid Cactus XtremeWeb ..etc. India I-Grid Japan Ninf DataFarm Korea... N*Grid Singapore NGP USA AppLeS Globus Legion Sun Grid Engine NASA IPG Condor-G Jxta NetSolve AccessGrid and many more... Cycle Stealing & .com Initiatives Distributed.net SETI@Home, …. Entropia, UD, SCS,…. Public Forums Global Grid Forum Australian Grid Forum IEEE TFCC CCGrid conference P2P conference Some Grid Projects & Initiatives http://www.gridcomputing.com

15. NetSolve mix-and-match Object-oriented Internet/partial-P2P Grid Computing Approaches Network enabled Solvers Economic-based Utility / Service-Oriented Computing Nimrod-G

16. The Gridbus Project @ Melbourne:Enable Leasing of IT Services on Demand Distributed Data WWG Gridbus World Wide Grid! On Demand Utility Computing

17. Grid Economy: Incentive, Resource Allocation and Regulation

18. GRACE: Service Oriented Grid Architecture GRid Architecture for Computational Economy (GRACE)

19. Grid Economy & Users’ Challenges • 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? • ….. • Grid Service Consumers (GSCs) • How do I decide expenses ? • 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? • ….. • They need mechanisms and technologies for value expression, value translation, and value enforcement.

20. Market-based Computing Systems Requirements • To enable users (GSPs and GSCs) to realise economic value, market-based systems need to provide mechanisms for: • Value Expression • a means to express their requirements, valuations, and objectives • Value Translation • scheduling policies to translate them to resource allocations • Value Enforcement • mechanisms to enforce the selection and allocation of differential services, and dynamic adaptation to changes in their availability at runtime • Market mechanisms, accounting and payment, Reservation of resources.

21. GRACE: A ReferenceService-Oriented Grid Architecture for Computational Economies Data Catalogue Grid Bank Information Service Grid Market Services Sign-on HealthMonitor Info ? Grid Node N … Grid Explorer … Secure ProgrammingEnvironments Job Control Agent Grid Node1 Applications Schedule Advisor QoS Pricing Algorithms Trade Server Trading Trade Manager Accounting Resource Reservation Misc. services … Deployment Agent JobExec Resource Allocation Storage Grid Resource Broker … R1 R2 Rm Grid Middleware Services Grid Consumer Grid Service Providers

22. Globus Grid Technologies for Realising the Vision High Energy Physics Brain Activity Analysis Grid Apps. Natural Language Engineering Molecular Docking Portfolio Analysis Grid Email High-level Services and Tools … User-LevelMiddleware (Grid Tools) Gridscape G-Monitor Programming Framework Grid Brokers & Schedulers Nimrod-G Gridbus Data Broker Alchemi: .NET Grid Services +Clustering of desktop PCs Data Management Services GridBank GMD Core Grid Middleware MDS GRAM GASS PKI-basedGrid Security Interface (GSI) .NET JVM Condor PBS SGE Libra Tomcat Grid Fabric Windows Linux AIX IRIX OSF1 HP UX Solaris

23. Application Code Explore data 1 Data Visual Application Composer 10 Results+Cost Info 2 GridResource Broker Data Catalogue 5 3 Grid Info Service Data Replicator (GDMP) 12 6 4 ASP Catalogue Grid Market Directory 9 7 Job Results 8 Grid Service (GS) (Globus) Bill Alchemi GS CPU orPE PE GTS 11 GridbusGridBank Cluster Scheduler PE GSP (Accounting Service) GSP (e.g., IBM) GSP (e.g., VPAC) GSP (e.g., UofM) On Demand Assembly of Services: Interaction Between Grid Components Data Source (Instruments/distributed sources) Cluster Scheduler PE Grid Service Provider (GSP)(e.g., CERN)

24. Creation and Operation of Virtual Enterprises Grid Market Directory Grid Bank

25. Grid Info. Grid Bank Grid Market Grid Market Service Service Directory (GMD) Directory (GMD) “ “ register me as GSP register me as GSP ” ” “ “ Give me list of Give me list of GSPs GSPs & price? & price? ” ” GTS GTS GTS ” ” service available? service available? “ “ “ “ Solve this in Solve this in Resource Resource (Grid Service Provider) (Grid Service Provider) 5hrs for $20 5hrs for $20 ” ” Broker Broker “ “ GTS GTS GTS (RB selects (RB selects GSPs GSPs ) ) “ “ service available? service available? (GSP) (GSP) ” ” service available? service available? GTS GTS GTS ” ” GTS GTS GTS GTS GTS GTS ( ( GTS GTS - - Grid Grid Trade Server) Trade Server) A Market-Oriented Grid Environment

26. Grid Market Infrastructure • Grids need to provide an infrastructure that supports: • (a) the creation of one or more GMP (Grid Marketplace) registries; • (b) the contributors to register themselves as GSPs along with their resources/application services that they wish to provide; • (c) GSPs to publish themselves in one or more GMPs along with service prices; and • (d) Grid resource brokers to discover resources/services and their attributes (e.g., access price and usage constraints) that meet user QoS requirements.

27. Grid Bank: Grid Transactions Authorization, Accounting, & Payment Infrastructure GridBank Server GridCheque + Resource Usage (GSC Account Charge GridCheque Establish Service Costs Applications Grid Trade Server GridBank Charging Module Grid Resource Broker (GRB) GridBank Payment Module GridCheque Resource Usage Grid Agent Grid Resource Meter User Deploy Agent and Submt Jobs Usage Agreement R1 R2 R3 R4 Grid Service Consumer (GSC) User Grid Service Provider (GSP)

28. Grid Brokering and Scheduling:A Grid Consumer Perspective Nimrod-G Broker: A Computational Grid Broker Gridbus Broker: A Grid Service Broker for Computational and Data Grid

29. Sample P-Sweep Applications Bioinformatics: Drug Design / Protein Modelling Natural Language Engineering Ecological Modelling: Control Strategies for Cattle Tick Sensitivityexperiments on smog formation Data Mining Electronic CAD: Field Programmable Gate Arrays High Energy Physics: Searching for Rare Events Computer Graphics: Ray Tracing Finance: Investment Risk Analysis VLSI Design: SPICE Simulations Civil Engineering: Building Design Network Simulation Automobile: Crash Simulation Aerospace: Wing Design astrophysics

30. Thesis: How to Construct and Deploy Applications on Global Grids ? • Three Options/Solutions: • Using pure Globus commands • Build your own Distributed App & Scheduler • Use Nimrod-G/Gridbus (Grid Brokers) • Perform parameter sweep (bag of tasks) (utilising distributed resources) within “T” hours or early and cost not exceeding $M.

31. Nimrod-G : A Grid Resource Broker[Buyya, Abramson, Giddy, 1999-2001] • A resource broker for managing, steering, and executing task farming (parameter sweep) applications on global Grids. • It allows dynamic leasing of resources at runtime based on their quality, cost, and availability, and users’ QoS requirements (deadline, budget, etc.) • Key Features • A declarative parameter programming language • A single window to manage & control experiment • Persistent and Programmable Task Farming Engine • Resource Discovery • Resource Trading • (User-Level) Scheduling & Predications • Generic Dispatcher & Grid Agents • Transportation of data & results • Steering & data management • Accounting

32. Nimrod-G Broker & Pluggable Scheduler Nimrod-G Client Nimrod-G Client Gridbus Client (Nirmrod-G or Gridbus Scheduler) Nimrod-G Farming Engine Schedule Advisor Trading Manager RecordKeeper Grid Dispatcher Data Catalog Grid Explorer Grid Middleware Globus, Gridbus Economy Services TM TS GE GIS Grid Info Server RM & TS RM & TS RM & TS G C L G Legion enabled node. Globus enabled node. L G C L RM: Local Resource Manager, TS: Trade Server Condor enabled node.

33. Grid Broker World-Wide Grid G-Monitor: A Grid Portal for Launching and Steering Applications

34. Data Catalogue Grid Info Server Nimrod-G Grid Broker Task Farming Engine Gridbus Scheduler Grid Trade Server Do this in 30 min. for $10? Grid Tools And Applications Nimrod Agent User Process Grid Node Resource Manager ProcessServer Grid Dispatcher File Server File access Nimrod-G and Gridbus Operations Grid Node Compute Node User Node

35. Deadline and Budget Constrained Scheduling Algorithms

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

37. On Demand Deployment of Applications on Global Grids A Case Study on Scheduling Applications on the World Wide Grid Network

38. World-Wide Grid Testbed

39. World-Wide Grid Testbed

40. Members of the Collaboration

41. Application Targets: SC 2003 HPC Challenge Demonstration • High Energy Physics – Melbourne School of Physics • Belle experiment – CP (charge parity) violation • Natural Language Engineering – Melbourne School of CS • Indexing Newswire Text • Protein Docking – WEHI for Medical Research, Melbourne • Screening molecules to identify their potential as drug candidates • Portfolio Analysis – UCM, Spain • Value at Risk/Investment risk analysis • Brain Activity Analysis – Osaka University, Japan • Identifying symptoms of common disorders through analysis of brain activity patterns. • Quantum Chemistry - Monash and SDSC effort • GAMESS

42. WW Grid Grid Experiment Testbed Setup Replica Catalogue @ UoM Physics Brokering Grid Node DataBroker: Melbourne U Nimrod-G: Monash U North America GMonitor Grid nodes US and Canadian Nodes Grid Broker Application Visualisation Internet (User Desktop) South America Australia Other Oz Grid Nodes Grid nodes in Brazil Asia Europe Grid nodes in China, India, Japan, Korea, Pakistan, Malaysia, Singapore, Taiwan, Grid nodes in UK, Germany, Netherlands, Poland, Cyprus, Czech Republic, Italy, Spain

43. Scheduling Evaluation Experiment Parameters • Workload: • 165 jobs, each need 5 minute of CPU time • Deadline: 2 hrs. and budget: 396000 G$ • Strategies: 1. Minimise Time 2. Minimise Cost • Experimental Results: • Optimise Cost: 115200 (G$) (finished in 2hrs.) • Optimise Time: 237000 (G$) (finished in 1.25 hr.) • In this experiment: Time-optimised scheduling execution costs double that of Cost-optimised. • Users can now trade-off between Time Vs. Cost.

44. Resources Selected & Price/CPU-sec.

45. Resource Scheduling for DBC Time Optimization Optimise Time: 237000 (G$) (finished in 1.25 hr.)

46. Resource Scheduling for DBC Cost Optimization • Optimise Cost: 115200 (G$) (finished in 2hrs.)

47. Belle Particle Physics Experiment:Australian Belle Analysis Data Grid • A running experiment based in KEK B-Factory, Japan • Investigating fundamental violation of symmetry in nature (Charge Parity) which may help explain the universal matter – antimatter imbalance. • Collaboration 400 people, 50 institutes • 100’s TB data currently • UoM School of Physics is an active participant and have led the Grid-enabling of the Belle data analysis framework. • Currently developing a Data Grid for High Energy Physics – ARC

48. Australian Belle Analysis Data Grid

49. Result Result File File Bookkeeper Network Information Service Job status feedback Gridbus Broker Local Local User Data Data process Grid Service Broker: A Gridbus Economic Scheduler for Data Grids Application & Data Parameterization Parameters Service node and Task list Broker User Interface Data Service Static or Dynamic Catalogue Parameters Resolver Resource Resource Task & data Discovery Catalogue requirements Grid Information Jobs Service Service nodes Grid Local Scheduler File Job schedule GASS Actuator & Monitor Server Broker Node Grid Node Remote Agent Data Host Remote Data Host2

50. Scheduling 100 Belle analysis jobs with: 1. ComputeGrid; 2. Data Gridstrategy 120 100 Compute Grid Centric Scheduling 80 Time (in Mins) 60 DataGrid Centric Scheduling 40 20 0 Scheduling limited to Resources with Data (Only 80 jobs processed as Adelaide node Compute service failed) Scheduling with Data optimization (Access remote data from optimal source if not available locally)