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A Periodic Portfolio Scheduler for Scientific Computing in the Data Center

A Periodic Portfolio Scheduler for Scientific Computing in the Data Center. Kefeng Deng, Ruben Verboon , Kaijun Ren , and Alexandru Iosup Parallel and Distributed Systems Group. JSSSP, IPDPS WS, Boston, MA, USA May 24, 2013.

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A Periodic Portfolio Scheduler for Scientific Computing in the Data Center

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  1. A Periodic Portfolio Scheduler for Scientific Computing in the Data Center Kefeng Deng, Ruben Verboon, KaijunRen, and AlexandruIosup Parallel and Distributed Systems Group JSSSP, IPDPS WS, Boston, MA, USA May 24, 2013

  2. Warm-Up Question:(2 minutes think-time + 2 minutes open discussion) • Think about own experience • Convince your partner before proposing an answer • Tell everyone the answer Q:What are the major issues of scheduling various types of workloads in current data centers?

  3. WhyPortfolio Scheduling? • Data centers increasingly popular • Constant deployment since mid-1990s • Users moving their computation to IaaS clouds • Consolidation efforts in mid- and large-scale companies • Old scheduling aspects • Hundreds of approaches, each targeting specific conditions—which? • No one-size-fits-all policy • New scheduling aspects • New workloads • New data center architectures • New cost models • Developing a scheduling policy is risky and ephemeral • Selecting a scheduling policy for your data center is difficult

  4. What is Portfolio Scheduling?In a Nutshell, for Data Centers • Create a set of scheduling policies • Resource provisioning and allocation policies, in this work • Online selection of the active policy, at important moments • Periodic selection, in this work • Same principle for other changes: pricing model, system, …

  5. Agenda • Why portfolio scheduling? • What is portfolio scheduling? In a nutshell… • Our periodic portfolio scheduler for the data center • Operational model • A portfolio scheduler architecture • The creation and selection components • Other design decisions • Experimental resultsHow useful is our portfolio scheduler? How does it work in practice? • Our ongoing work on portfolio scheduling • How novel is our portfolio scheduler? A comparison with related work • Conclusion

  6. Background InformationOperational Model • Single data center • VM pool per user • Provisioning and allocation of resources via policies • Issues orthogonal to this model: failures, pre-emption, migration, … Whichpolicy? Which resources?

  7. Portfolio SchedulingThe Process Whichpolicies to include? Whichpolicy to activate? Creation Selection Reflection Application Whichchanges to the portfolio? Which resources?What to log?

  8. Portfolio SchedulingComponentsCreation • Schedulingpolicy = (provisioning, job selection) tuple • We assume in thiswork all VMs are equal and exclusivelyused(no VM selectionpolicy—we study these in otherwork) • Provisioningpolicies • Start-Up: all resources availablefrom start to finish of execution (classic) • On-Demand, Single VM (ODS): onenew VM foreachqueued job • On-Demand, Geometric (ODG): grow-shrinkexponentially • On-Demand, Execution Time (ODE): lease according to estimation of queuedruntime (useshistoricalinformation and a predictor) • On-Demand, Wait Time (ODW): leasesonlyfor jobs with high waittimes • On-Demand, XFactor (ODX): tries to ensure constant slowdown, via observedwait time and estimated run time • Job selectionpolicies • FCFS, SJF (assumesknownorwell-estimatedrun-times) Deng, Song, Ren, and Iosup. Exploring Portfolio Scheduling for Long-term Execution of Scientific Workloads in IaaS Clouds. Submitted to SC|13.

  9. Portfolio SchedulingComponentsSelection • Periodicexecution • Simulation-basedselection • Utility function • Alternatives simulator • Expert humanknowledge • Running workload sample in similar environment, underdifferent policies • mathematicalanalysis • Alternatives utility function • Well-known and exoticfunctions α=β=1 Κ=100 RJ: Total Runtime of Jobs RV: Total Runtime of VMs S: Slowdown Agmon Ben-Yehuda, Schuster, Sharov, Silberstein, Iosup.ExPERT: pareto-efficient task replication on grids and a cloud. IPDPS’12.

  10. Putting it all togetherOur Portfolio Scheduler (1) Creation (2) Selection (3) Application (4) Reflection

  11. Agenda • Why portfolio scheduling? • What is portfolio scheduling? In a nutshell… • Our periodic portfolio scheduler for the data center • Experimental results • Experimental Setup • How useful is our portfolio scheduler? • How does it work in practice? • Our ongoing work on portfolio scheduling • How novel is our portfolio scheduler? A comparison with related work • Conclusion

  12. ExperimentalSetupSimulator and Metrics • The DGSim simulator • Since 2007 • Scheduling in single- and multi-clustergrids • Scheduling in IaaSclouds • Metrics • Average Job Wait-Time • Average Job Slowdown • Resource utilization • ChargedCost • Utility Iosup, Sonmez, Epema. DGSim: Comparing Grid Resource Management Architectures through Trace-Based Simulation. Euro-Par 2008.

  13. Experimental SetupSynthetic and Real Traces • Synthetic Workloads: 5 arrival patterns • Real Trace: ANL Intrepid 2009 • 8 months • 68,936 jobs

  14. ExperimentalResults, SyntheticWorkloadsResource Utilization + Workload Utility • POrtfolioleads to better utility • Start-Upleads to poor utility • POrtfolioleads to high utilization • Start-Upleads to poorutilization

  15. ExperimentalResults, ANL IntrepidWorkloadCost + Utilization + Utility • POrtfolionot best foreachmetric • POrtfolioleads to low cost • POrtfolioleads to high utilization • POrtfolioleads to high utility (slowdown-utilizationcompound)

  16. ExperimentalResultsOperation of the Portfolio Scheduler • Policychangefollowsarrivalpattern • ANL-IntrepidbetweenSteady and Periodic

  17. ExperimentalResultsOperation of the Portfolio Scheduler • No single policy is alwaysselectedfor the sameworkload • Different workloads, different top-3 policies

  18. Agenda • Why portfolio scheduling? • What is portfolio scheduling? In a nutshell… • Our periodic portfolio scheduler for the data center • Experimental results • Our ongoing work on portfolio scheduling • Application also to online gaming, more complex scheduling policies • Algorithm for selection under limited selection time, utility functions • How novel is our portfolio scheduler? A comparison with related work • Conclusion

  19. Portfolio Schedulingfor Online Gaming(alsoforScientificWorkloads) • CoH= Cloud-based, online, Hybrid scheduling • Intuition: keep rental cost low by finding good mix of machine configurations and billing options • Main idea: portfolio scheduler = run both solver of an Integer Programming Problem and various heuristics, then pick best schedule at deadline • Additional feature: Can use reserved cloud instances • Promising early results, for Gaming (and scientific) workloads Shen, Deng, Iosup, and Epema. Scheduling Jobs in the Cloud Using On-demand and Reserved Instances, EuroPar’13.

  20. Ongoing Work Job run time estimation Different traces (4) Maximum simulation time (5) Time interval for portfolio simulation (3) Different selection criteria Deng, Song, Ren, and Iosup. Exploring Portfolio Scheduling for Long-term Execution of Scientific Workloads in IaaS Clouds. Submitted to SC|13.

  21. Agenda • Why portfolio scheduling? • What is portfolio scheduling? In a nutshell… • Our periodic portfolio scheduler for the data center • Experimental results • Our ongoing work on portfolio scheduling • How novel is our portfolio scheduler? A comparison with related work • Conclusion

  22. Conclusion Take-Home Message • A.Iosup@tudelft.nl- DengKefeng@nudt.edu • http://www.st.ewi.tudelft.nl/~iosup/- http://www.pds.ewi.tudelft.nl/ • Portfolio Scheduling = set of scheduling policies, online selection • Creation, Selection, Application, Reflection • Periodic portfolio scheduler for data centers • Explored Creation and, especially, Selection • Good results for synthetic and real traces • Easy to setup, easy to trust • JSSPP’13, EuroPar’13, SC’13 (?) • Reality Check (future work): we will apply it in the DAS AlexandruIosup

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