Apache Hadoop India Summit 2011 talk "An Extension of Fairshare-Scheduler and a Novel SLA based Learning Scheduler in Hadoop" by G Sudha Sadhasivam and Priya N
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Apache Hadoop India Summit 2011 talk "An Extension of Fairshare-Scheduler and a Novel SLA based Learning Scheduler in Hadoop" by G Sudha Sadhasivam and Priya N
- 1. AN EXTENSION OF FAIRSHARESCHEDULER AND A NOVEL SLA BASED LEARNING SCHEDULER IN HADOOP BY Dr G SUDHA SADHASIVAM PROFESSOR & PRIYA N STUDENTPSG COLLEGE OF TECHNOLOGY COIMBATORE
- 2. agenda Introduction - Metascheduler in Fairsharescheduler. Features. Extended Fairscheduler Architecture. Work Flow. Experimental results. Learning Scheduler with SLA. Design of Proposed System. Work Flow
- 4. ARCHITECTURE Node 1 USER 1 Node 2 Pool USER 2 FAIRSHARE SCHEDULER Node 3 USER 3 LARGE JOB FIRST+ SMALL JOB BACKFILLING Node 4 USER 4
- 7. Weight = weight *priorityfactor.
- 11. RESULT(LFSB) :Different Jobs
- 12. More small jobs
- 13. A Novel sla based learning scheduler
- 14. Schedulers IN Hadoop Hadoop on Demand – FIFO with Torque No data locality Fairshare Fairshares resources among jobs in pools Excess resources are shored between pools Capacity Fairsharing among organisations Inter queue priority is maintained manually (not dynamic) Dynamic priority scheduler Adjustable priority dynamically Demand / budget of the user More priority for smaller jobs Large jobs have to be broken up into smaller ones
- 15. PATCHES Security features to isolate users Launching multuple tasks per heartbeat Parallelise jobs and launch smaller jobs faster Prevent oversubscribing nodes (only fter job submission) – RAM / HD
- 17. Task assignment right node.
- 18. No policies and less user level response.
- 20. SLA :user specifying requirements.
- 21. Job executing at right node.
- 23. Classification based on Job traces History (Learning).
- 24. Creation of Queues for jobs as I/O and CPU
- 29. Workflow of Scheduler Node features CLASSIFIER Job Features+SLA (MIS+MOS)/MTCT >Avg.Disk I/o rate Job Traces history RIGHT NODE& Job type Calculate &Compare Utility Change priority I/O or CPU I/O queue CPU queue
- 30. example Node Feature value
- 31. Job Submitted (Job Features) ram=400Mb,HD=100Gb, M=6,R=2 ram=500Mb. HD=120Gb M=8 R=0. P(node)={no. job Features+no.node features*(P(F1)+P(F2), …P(Fn))}/Total features P(J1M1)=1,P(J1M2)=0.875 ,P(J1M3)=0.8,P(J1M4)=1, P(J1M5)=1, P(J1M6)=0.625. P(J2M1)=1,P(J1M2)=0.857 ,P(J1M3)=0.857,P(J1M4)=0.514, P(J1M5)=0.857, P(J1M6)=0.514 JOB 1= M1,M4,M5. M4 satisfies. JOB 2= M1.
- 32. CPU or I/O bound JOB I/O rate : 10 Mbytes / sec MTCT : 10 sec
- 35. Juan Wang, Wenming Guo, ”The Application of Backfilling in Cluster Systems”,2009 IEEE International Conference on Communication and Mobile Computing.
- 36. Jaideep Dhok and Vasudeva Varma “Using Pattern Classification for Task Assignment in Map Reduce”. 10th IEEE/ACM International Conference CCGrid 2010.
- 37. Amy W. Apon, Thomas D.Wagner, and Lawrence. Dowdy. “A learning approach to processor allocation in parallel systems”. In CIKM ’99:Proceedings of the eighth international conference on Information and knowledge management, pages 531–537, New York, NY, USA, 1999.