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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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

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  • 1. AN EXTENSION OF FAIRSHARESCHEDULER AND A NOVEL SLA BASED LEARNING SCHEDULER IN HADOOP<br /> BY<br />Dr G SUDHA SADHASIVAM<br />PROFESSOR<br />&<br />PRIYA N<br />STUDENTPSG COLLEGE OF TECHNOLOGY COIMBATORE<br />
  • 2. agenda<br />Introduction<br /> - Metascheduler in Fairsharescheduler.<br />Features.<br />Extended Fairscheduler Architecture.<br />Work Flow.<br />Experimental results.<br />Learning Scheduler with SLA.<br />Design of Proposed System.<br />Work Flow<br />
  • 3. Fairshare scheduler<br />Existing System :-<br /><ul><li> Jobs in pool are executed in Fairshare manner.</li></ul>Proposed System :-<br /><ul><li> Fairshare Execution of Jobs from pool such that Large Job first and Small Job Backfilling.</li></li></ul><li>FEAtures<br />Jobs in pools<br />Guaranteed capacity<br />Minimum Shares<br />Job Limits<br />Job Priorities<br />Pool Weights<br />
  • 4. ARCHITECTURE<br />Node 1<br />USER 1<br />Node 2<br />Pool<br />USER 2<br />FAIRSHARE SCHEDULER<br />Node 3<br />USER 3<br />LARGE JOB FIRST+ SMALL JOB BACKFILLING<br />Node 4<br />USER 4<br />
  • 5. Calculate<br /><ul><li>User Estimated time = (no.of maps *maptime)+(no.of reduces * reduce time).</li></ul>Update<br /><ul><li>Runnability
  • 6. Taskcount=total_Tasks–running_Tasks–finished_Tasks+needed_Tasks_for_job
  • 7. Weight = weight *priorityfactor.
  • 8. Fairshare=(weight *oldslots)/totalweight
  • 9. Deficit (MR_Deficit) =(fairshare - running) *timedelta</li></li></ul><li>WORKFLOW<br />= no.of maps * maptime+no.of reduces * reduce time<br />Calculate no. Of maps and reduces<br />Find User Estimated Time<br />Create a list of jobs<br />Get Jobs in pool<br />Finished/running<br />fairscheduler.start()<br />Get runstate of job in progress<br />Remove from list<br />Categorize jobs as small and large<br />Update:-<br />Weight,taskcount,min.slots,runnability,fairshare<br />Job finish time<user estimated time<br />Bring large job first and backfill small jobs<br />Backfill if exe_time<delay<br />
  • 10.
  • 11. RESULT(LFSB) :Different Jobs<br />
  • 12. More small jobs<br />
  • 13. A Novel sla based learning scheduler<br />
  • 14. Schedulers IN Hadoop<br />Hadoop on Demand – <br />FIFO with Torque<br />No data locality<br />Fairshare<br />Fairshares resources among jobs in pools<br />Excess resources are shored between pools<br />Capacity<br />Fairsharing among organisations<br />Inter queue priority is maintained manually (not dynamic)<br />Dynamic priority scheduler<br />Adjustable priority dynamically<br />Demand / budget of the user<br />More priority for smaller jobs<br />Large jobs have to be broken up into smaller ones<br />
  • 15. PATCHES<br />Security features to isolate users<br />Launching multuple tasks per heartbeat<br />Parallelise jobs and launch smaller jobs faster<br />Prevent oversubscribing nodes (only fter job submission) – RAM / HD<br />
  • 16. <ul><li>Existing System:-
  • 17. Task assignment right node.
  • 18. No policies and less user level response.
  • 19. Proposed System :-
  • 20. SLA :user specifying requirements.
  • 21. Job executing at right node.
  • 22. Classify jobs as I/O bound or cpu bound – priority and assign jobs</li></li></ul><li>Proposed methodology<br />SLA – User details ,job requirements and charge sheet.<br />Scheduler:<br /><ul><li>Classifies jobs based on (SLA+Job Features) and node features.(new job)
  • 23. Classification based on Job traces History (Learning).
  • 24. Creation of Queues for jobs as I/O and CPU
  • 25. Assignment to Queues based on Utility Function. </li></li></ul><li>Gather all node details & check for SLA approval. If Yes allow to submit jobs. <br />Owner,Description,User details and requirements<br />Node 1<br />Node 2<br />SLA<br />USER<br />Node 3<br />LEARNING SCHEDULER<br />Node 4<br />Node 5<br />
  • 26.
  • 27.
  • 28.
  • 29. Workflow of Scheduler<br />Node features<br />CLASSIFIER<br />Job Features+SLA<br />(MIS+MOS)/MTCT >Avg.Disk I/o rate<br />Job Traces history<br />RIGHT NODE& Job type<br />Calculate &Compare Utility<br />Change priority<br />I/O or CPU<br />I/O queue<br />CPU queue<br />
  • 30. example<br />Node Feature value<br />
  • 31. Job Submitted (Job Features)<br />ram=400Mb,HD=100Gb, M=6,R=2<br />ram=500Mb. HD=120Gb M=8 R=0.<br />P(node)={no. job Features+no.node features*(P(F1)+P(F2), …P(Fn))}/Total features<br />P(J1M1)=1,P(J1M2)=0.875 ,P(J1M3)=0.8,P(J1M4)=1, P(J1M5)=1, P(J1M6)=0.625.<br />P(J2M1)=1,P(J1M2)=0.857 ,P(J1M3)=0.857,P(J1M4)=0.514, P(J1M5)=0.857, P(J1M6)=0.514<br />JOB 1= M1,M4,M5. M4 satisfies.<br />JOB 2= M1.<br />
  • 32. CPU or I/O bound JOB<br />I/O rate : 10 Mbytes / sec<br />MTCT : 10 sec<br />
  • 33. Scheduler<br /><ul><li>Find the right node for the job using a classifier.</li></ul> :Naïve Bayes classifier<br /><ul><li>Find the Job type whether I/O or CPU bound.</li></ul>(MIS+MOS)/MTCT >Avg.Disk I/O rate<br /><ul><li>Calculate the Utility Function value.</li></ul> FIFO,Deficit,SJF.<br /><ul><li>Pass the jobs to the queue.</li></li></ul><li>Advantages<br /><ul><li>Fairscheduler with Backfilling improves on waiting time for large jobs. It introduces “no starvation” slogan and improves response time.
  • 34. SLA based scheduler brings high user level response and better utilization of resources.</li></li></ul><li>References<br /><ul><li> Saeed Iqbal ,Rinku Gupta, Yung chin Fang “Job Scheduling in HPC clusters” DELL Power Solutions 2005.
  • 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.
  • 38. Harry Zhang. “The Optimality of Naive Bayes”. In Valerie Barr and Zdravko Markov, editors, FLAIRS Conference. AAAI Press, 2004.</li></li></ul><li>THANK YOU<br />

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