High-Performance Schedulers Francine Berman

1. High-Performance SchedulersFrancine Berman CSCI 599 Grid Computing Class Presentation Caimu Tang High-Performance Schedukers by Francine Berman

2. Why scheduling • Optimize Performance: execution time, throughput, fairness and etc. (QoS) • Load balancing. • Help to design an effective program model. • Ubiquity. process scheduling in operating system, task scheduling in parallel computing and scheduling in real life too. High-Performance Schedukers by Francine Berman

3. Scheduling in GRID • Application level. • resource e.g. data, communication bandwidth. • Models, scheduling policy, program model, performance model, performance measurement. • Current performance measure, minimize execution time. High-Performance Schedukers by Francine Berman

4. Requirements on GRID scheduling model • Adaptive to the dynamic environment. • Adaptive to the varying performance metrics upon the course of application execution. • Performance predictions over time. • Coarse and fine-tuning the component parameters. High-Performance Schedukers by Francine Berman

5. Techniques commonly employed • Parameterize the components in an application. • Make use of dynamic information, e.g. CPU slots available percentage, network bandwidth available percentage. • Compositional scheduling model, structural character of application and dynamic interaction with grid environment. High-Performance Schedukers by Francine Berman

6. Program Model • Data-flow-style program graphs. Program dependency graph, dependency graph based on task phase,or coarse-grained task dependency graph. • Application is represented by its characteristics. Resource requirements, some problem in some specific domain. High-Performance Schedukers by Francine Berman

7. Example: Task Dependency Graph High-Performance Schedukers by Francine Berman

8. Performance Model • Scheduler-driven performance model. Using dynamic information, skeleton performance model, last program iteration as benchmark, using offline mapping indexed by run-time information. • User-driven performance model. Using static and dynamic information, depending on programmer to use the system characteristics. High-Performance Schedukers by Francine Berman

9. Scheduling Policy • Choose a set of resources to achieve the performance goal. • Fist Come, First Serve. • Preemptive. • Fair Queuing. • And etc. High-Performance Schedukers by Francine Berman

10. AppLes: Application-Level Scheduler • Everything evaluated in terms of the impact on the application, so the resources are evaluated in terms of the predicted capacities and their potential for requirements. • No resource manager is assumed. • On User-level, no specific privilege required. • Heterogeneous and cross organization. • Depends on use Network Weather Service for the dynamic resource load and availability. High-Performance Schedukers by Francine Berman

11. AppLes(Cont’d) • Information gathered by the network weather service is used to parameterize performance models and to predict the state of grid resources at the time the application will be scheduled. • Time balancing, all processors are assigned some possibly nonuniform amount of the goal that they will all finish at roughly the same time. • Compositional component models is deployed. • Adaptive scheduling scheme. High-Performance Schedukers by Francine Berman

12. DSSA:Digital Sky Survey Analysis • DAS, Data Assimilation System, Characteristics. • I/O Bound • Huge bandwidth for data transfer • Data represenation (Data set, metadata) • Data federation. • DSS • Huge amount of image data (250GB of metadata per single plate). • Data set can be extracted from the various database for reanalysis and possibly create new high integrity data set. • Data curation/validation will be performed on the dataset upon needs. • Multiple data repositories. Database federation. High-Performance Schedukers by Francine Berman

13. Scheduling DSSA using AppLeS • DSSA, digitization, curation and validation of photographic plates for archiving, querying and etc. (metadata, dataset replication, dataset database federation) • Communication resource scheduling using dynamic information provided by Network Weather Service. • Compositional performance model. (Metainformation, parameters for dynamic resource information). • Select the candidate to minimize the execution time. • Actuate the selected schedule. • AppLes may help DSSA to modify to adapt AppLes and achieve overall high performance. High-Performance Schedukers by Francine Berman

14. Trends • Using dynamic information. (adaptation) • Using metainformation. (QoIn) • Using realistic programs, more application code specific. • Restricting domain, more domain specific. • Develop language interface, automating scheduling process. High-Performance Schedukers by Francine Berman

15. Challenges • Portability vs. Performance, minimize the performance impact of architecture independence. • Grid-Aware programming, using High-Performance scheduler and leverage the performance potential of grid environment. • Scalability. High-Performance Schedukers by Francine Berman

16. Challenges (Cont’d) • Efficiency, scheduler overhead should not affect the normal application execution or should be kept at minimal level possible. • Repeatability, i.e. Consistency, predictability. • Multischeduling, cooperating with resource schedulers, stability and no thrashing. High-Performance Schedukers by Francine Berman

17. System Support • Dynamic monitoring mechanisms, information persistency, provide faithful information to scheduler. (extensible and flexible). • High-level language support. Provide uniform semantics cross computational grids, flexible so that low level service may change so long as the high-level semantics is consistent. • Integration with other software tools. • Assistance for multischeduling, information interfaces, data synchronization, High-Performance Schedukers by Francine Berman

18. Conclusion • Scheduling is the key for performance in grid environment. • Coordinating resources in grid environment • Most advanced grid application are targeted to specific resources. • High-Performance Scheduling Evolution. High-Performance Schedukers by Francine Berman