1. Superscheduling and Resource Brokering Sven Groot (0024821)

2. Grid Information Service Not all information available Grid Information System –Globus Monitoring and Discovery Service (MDS2) –Grid Monitoring Architecture (GMA) Common features –Organise sensors –Static vs. Dynamic data –Extensible –Agreed upon schema

3. Stages of Grid Scheduling Phase 1: Resource Discovery –Authorization filtering –Application Requirement Definition –Minimal requirement filtering

4. Stages of Grid Scheduling (2) Phase 2: System Selection –Dynamic information gathering –System Selection

5. Stages of Grid Scheduling (3) Phase 3: Job Execution –Advance Reservation (optional) –Job Submission –Preparation Tasks –Monitoring Progress –Job Completion –Cleanup Tasks

6. Application requirements

7. Application Requirements (2) General requirements –Compute-related requirements –Data-related requirements –Network-related requirements

8. Application Requirements (3) Challenges –Application deployment –Metacomputing –Predicting performance Theoretical prediction History based prediction Testcase-based prediction –Adaptive brokering

9. Application Requirements (4) Related issues –Application frameworks –Virtual Organizations –Security requirements –Accounting policies –User preferences

10. Scheduling in GrADS Scheduling phases –Launch-time scheduling –Rescheduling –Meta-scheduling

11. GrADS

12. GrADS (2) Focus applications –ScaLAPACK –Cactus –FASTA –Iterative applications Jacobi method Game of Life Fish

13. GrADS: Launch-time scheduling

14. GrADS: Launch-time scheduling (2) Configurable Object Program –Application requirements definition AART ClassAds Redline

15. ClassAds sample

16. GrADS: Launch-time scheduling (3) Performance model –General method develop an analytic model for well-understood aspects of applicatio or system performance test the analytic model against achieved application performance develop empirical models for poorly-understood aspects of application or system behavior –Some application specific methods –Implemented as shared libraries

17. GrADS: Launch-time scheduling (4) Mapper –Maps data and/or tasks to resources –Different mapping methods Equal allocation Time balancing Data locality

18. GrADS: Launch-time scheduling (5) Search procedure –General steps identify a large number of sets of resources that may be good platforms for the application use the application-specific mapper and performance model to generate a data map and predicted execution time for those resource sets select the resource set that results in the lowest predicted execution time

19. GrADS: Launch-time scheduling (6) Resource-aware search

20. GrADS: Launch-time scheduling (6) Simulated Annealing

21. GrADS: Rescheduling Additional complexities –Lack of built-in mechanisms –Need to distinguish processors that are running/not running the current process –Overheads can be high

22. GrADS: Rescheduling (2)

23. Rescheduling methods –Application migration –Process swapping

24. GrADS: Metascheduling

25. Grid Service Level Agreements Contract –Provide some capability –Perform some task Types of SLAs –Resource Service Level Agreements –Task Service Level Agreements –Binding Service Level Agreements

26. Grid SLAs (2)

27. Grid SLAs (3) Motivating scenarios –Community Scheduler Scenario

28. Grid SLAs (4) Motivating scenarios (cont’d) –File transfer scenario

29. Grid SLAs Resource virtualization

30. Multicriteria Basic definitions –Pareto Dominance –Pareto Optimality –Pareto-optimal set –Pareto Front

31. Multicriteria (2) Motivations –Various stakeholders and their preferences –Job scheduling –Application-Level scheduling –Hard constraints and soft constraints

32. Multicriteria (3) Approach –Criteria Related to stakeholders Related to entire system Time criteria Cost criteria Resource utilization criteria –Modeling preferences

33. Multicriteria (4) Selection method –Rule-based system requirements Expression of policies Execution of different scheduling procedures Adaptation to the environment Selection of the best solution –Multicriteria optimization

35. Example (cont’d)

36. Aggregate criteria –End user satisfaction –Resource Owner Satisfaction –VO overall performance

37. Example (cont’d)