1. 1 520 Student Presentation GridSim – Grid Modeling and Simulation Toolkit

2. 2 Outline Introduction GRACE Framework Nimrod-G Resource Broker GridSim Grid Resource Scheduling Simulation Toolkit

3. 3 Introduction Inspired from electrical power Grid Computational power Grid Sharing, aggregation geographically distributed resources Low level services(security, information, directory, resource management) High level services(resource discovery, cost negotiation, resource selection)

4. 4 GRACE Framework Grid Architecture for Computational Economy builds on the existing Grid systems new services for resource management and trading and aggregation

5. 5 A generic Grid architecture for computational economy

6. 6 Commodity Market Models GRP specify their service price charge users according to the amount of resource consumed GRP submit price specification to GTS CPU cycles, storage, software, and network

7. 7 Nimrod-G Resource Broker economic-based resource management and scheduling algorithms user-defined deadline and budget constraints schedule optimizations and manages supply and demand

8. 8 Architecture of Nimrod-G system TFE –creation of jobs –job status The scheduler –resource discovery –resource trading –resource selection –job assignment Dispatcher –deploys jobs Agents –setting up execution environment –transporting the code and data

9. 9 The Flow actions in Nimrod-G

10. 10 Economic-based Scheduling Algorithms Cost Optimization –1. Sort resources. –2. assign jobs in turn, without exceeding the deadline. Time Optimization –1. For each resource, calculate the next completion time for an assigned job –2. Sort resources by next completion time. –3. Assign one job to the first resource for which the cost is within the budget. –4. Repeat steps 1-3 until done.

11. 11 World-Wide Grid (WWG) Testbed

12. 12 Parameter Sweep Application Create a set of jobs with different parameters Calculate the angular values from different degrees

13. 13 Cost Optimisation Scheduling Australian peak timeAustralian off-peak time

14. 14 Cost Optimisation Scheduling Australian peak time Australian off-peak time

15. 15 Cost and Time Optimization Scheduling

16. 16 Cost and Time Optimization Scheduling time optimization schedulingcost optimization scheduling

17. 17 The need for Simulation Tools real testbed is not available expensive and time consuming limited to a few resources and domains testing scheduling algorithms for scalability and adaptability scheduler performance is hard to trace

18. 18 The core Entities of GridSim

19. 19 Event diagram among entities internal event external event synchronous event asynchronous event

20. 20 Nimrod-G simulation 1. A set of Gridlets 2. Find resources and their costs, create resource list 3. Select scheduling policy based on user requirement 4. Dispatch Gridlets to resources 5. Submits Gridlets to resources 6. Updates runtime parameter to help predict job consumption rate 7. Repeat 3-6 till finish all jobs or exceed deadline or budget

21. 21 WWG testbed resources simulated using GridSim

22. 22 Cost-optimization scheduling

23. 23 Cost-optimization scheduling

24. 24 Time Optimization Scheduling

25. 25 Comparing the Cost and Time Optimization Scheduling

26. 26 1. Sort resources in increasing order 2. assign jobs in turn, without exceeding the deadline 3. If more two or more resources have the same cost and capacity, schedule resources based on the Time Optimization algorithm Cost-Time Optimization Scheduling

27. 27 Cost-Time Optimization Scheduling

28. 28 Cost vs. Cost-Time optimization scheduling

29. 29 Cost vs. Cost-Time optimization scheduling

30. 30 Cost vs. Cost-Time optimization scheduling

31. 31 Conclusion The Cost-Time optimization algorithm is more efficient than Cost optimization algorithm. To choose Cost-Time optimization or to choose Time optimization algorithm depends on your deadline and budget.

32. 32 Reference 1.R. Buyya, D. Abramson, and J. Giddy, A Case for Economy Grid Architecture for Service-Oriented Grid Computing, Proceedings of the International Parallel and Distributed Processing Symposium:10th IEEE International Heterogeneous Computing Workshop (HCW 2001), April 23, 2001, San Francisco, California, USA, IEEE CS Press, USA, 2001. 2.R. Buyya, D. Abramson, and J. Giddy, Nimrod-G: An Architecture for a Resource Management and Scheduling System in a Global Computational Grid, The 4th International Conference on High Performance Computing in Asia-Pacific Region (HPC Asia 2000), May 2000, Beijing, China, IEEE Computer Society Press, USA. 3.R. Buyya, J. Giddy, D. Abramson, An Evaluation of Economy-based Resource Trading and Scheduling on Computational Power Grids for Parameter Sweep Applications, Proceedings of the 2nd International Workshop on Active Middleware Services (AMS 2000), Kluwer Academic Press, August 1, 2000, Pittsburgh, USA. 4.R. Buyya, M. Murshed, and D. Abramson, A Deadline and Budget Constrained Cost-Time Optimization Algorithm for Scheduling Task Farming Applications on Global Grids, Technical Report, Monash University, March 2002. http://www.buyya.com/gridsim/ http://www.buyya.com/gridsim/ 5.Rajkumar Buyya and Manzur Murshed, GridSim: A Toolkit for the Modeling and Simulation of Distributed Resource Management and Scheduling for Grid Computing, The Journal of Concurrency and Computation: Practice and Experience (CCPE), Volume 14, Issue 13-15, Wiley Press, Nov.-Dec., 2002.