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1 TUD-PDS A Periodic Portfolio Scheduler for Scientific Computing in the Data Center Kefeng Deng, Ruben Verboon, Kaijun Ren, and Alexandru Iosup Parallel.

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Presentation on theme: "1 TUD-PDS A Periodic Portfolio Scheduler for Scientific Computing in the Data Center Kefeng Deng, Ruben Verboon, Kaijun Ren, and Alexandru Iosup Parallel."— Presentation transcript:

1 1 TUD-PDS 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

2 2 PDS Group, TUD 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 3 PDS Group, TUD 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 Why Portfolio Scheduling?

4 4 PDS Group, TUD 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 5 PDS Group, TUD Agenda 1.Why portfolio scheduling? 2.What is portfolio scheduling? In a nutshell… 3.Our periodic portfolio scheduler for the data center 1.Operational model 2.A portfolio scheduler architecture 3.The creation and selection components 4.Other design decisions 4.Experimental results How useful is our portfolio scheduler? How does it work in practice? 5.Our ongoing work on portfolio scheduling 6.How novel is our portfolio scheduler? A comparison with related work 7.Conclusion

6 6 PDS Group, TUD Background Information Operational Model Single data center VM pool per user Provisioning and allocation of resources via policies Issues orthogonal to this model: failures, pre-emption, migration, … Which policy? Which resources?

7 7 PDS Group, TUD Portfolio Scheduling The Process CreationSelection ReflectionApplication Which policies to include?Which policy to activate? Which resources? What to log?Which changes to the portfolio?

8 8 PDS Group, TUD Portfolio Scheduling Components Creation Scheduling policy = (provisioning, job selection) tuple We assume in this work all VMs are equal and exclusively used (no VM selection policy—we study these in other work) Provisioning policies Start-Up: all resources available from start to finish of execution (classic) On-Demand, Single VM (ODS): one new VM for each queued job On-Demand, Geometric (ODG): grow-shrink exponentially On-Demand, Execution Time (ODE): lease according to estimation of queued runtime (uses historical information and a predictor) On-Demand, Wait Time (ODW): leases only for jobs with high wait times On-Demand, XFactor (ODX): tries to ensure constant slowdown, via observed wait time and estimated run time Job selection policies FCFS, SJF (assumes known or well-estimated run-times) Deng, Song, Ren, and Iosup. Exploring Portfolio Scheduling for Long-term Execution of Scientific Workloads in IaaS Clouds. Submitted to SC|13.

9 9 PDS Group, TUD Portfolio Scheduling Components Selection Periodic execution Simulation-based selection Utility function Alternatives simulator Expert human knowledge Running workload sample in similar environment, under different policies mathematical analysis Alternatives utility function Well-known and exotic functions Agmon Ben-Yehuda, Schuster, Sharov, Silberstein, Iosup. ExPERT: pareto-efficient task replication on grids and a cloud. IPDPS’12. α=β=1 Κ=100 R J : Total Runtime of Jobs R V : Total Runtime of VMs S: Slowdown

10 10 PDS Group, TUD Putting it all together Our Portfolio Scheduler (1) Creation (2) Selection (3) Application (4) Reflection

11 11 PDS Group, TUD Agenda 1.Why portfolio scheduling? 2.What is portfolio scheduling? In a nutshell… 3.Our periodic portfolio scheduler for the data center 4.Experimental results 1.Experimental Setup 2.How useful is our portfolio scheduler? 3.How does it work in practice? 5.Our ongoing work on portfolio scheduling 6.How novel is our portfolio scheduler? A comparison with related work 7.Conclusion

12 12 PDS Group, TUD Experimental Setup Simulator and Metrics The DGSim simulator Since 2007 Scheduling in single- and multi-cluster grids Scheduling in IaaS clouds Metrics Average Job Wait-Time Average Job Slowdown Resource utilization Charged Cost Utility Iosup, Sonmez, Epema. DGSim: Comparing Grid Resource Management Architectures through Trace-Based Simulation. Euro-Par 2008.

13 13 PDS Group, TUD Experimental Setup Synthetic and Real Traces Synthetic Workloads: 5 arrival patterns Real Trace: ANL Intrepid 2009 8 months 68,936 jobs

14 14 PDS Group, TUD Experimental Results, Synthetic Workloads Resource Utilization + Workload Utility POrtfolio leads to better utility Start-Up leads to poor utility POrtfolio leads to high utilization Start-Up leads to poor utilization

15 15 PDS Group, TUD Experimental Results, ANL Intrepid Workload Cost + Utilization + Utility POrtfolio not best for each metric POrtfolio leads to low cost POrtfolio leads to high utilization POrtfolio leads to high utility (slowdown-utilization compound)

16 16 PDS Group, TUD Experimental Results Operation of the Portfolio Scheduler Policy change follows arrival pattern ANL-Intrepid between Steady and Periodic

17 17 PDS Group, TUD Experimental Results Operation of the Portfolio Scheduler No single policy is always selected for the same workload Different workloads, different top-3 policies

18 18 PDS Group, TUD Agenda 1.Why portfolio scheduling? 2.What is portfolio scheduling? In a nutshell… 3.Our periodic portfolio scheduler for the data center 4.Experimental results 5.Our ongoing work on portfolio scheduling 1.Application also to online gaming, more complex scheduling policies 2.Algorithm for selection under limited selection time, utility functions 6.How novel is our portfolio scheduler? A comparison with related work 7.Conclusion

19 19 PDS Group, TUD Portfolio Scheduling for Online Gaming (also for Scientific Workloads) 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 20 PDS Group, TUD Ongoing Work (1)Job run time estimation (2)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 21 PDS Group, TUD Agenda 1.Why portfolio scheduling? 2.What is portfolio scheduling? In a nutshell… 3.Our periodic portfolio scheduler for the data center 4.Experimental results 5.Our ongoing work on portfolio scheduling 6.How novel is our portfolio scheduler? A comparison with related work 7.Conclusion

22 22 PDS Group, TUD Related Work Computational portfolio design Huberman’97, Streeter et al.’07 ’12, Bougeret’09, Goldman’12, Gagliolo et al.’06 ’11, Feitelson et al. JSSPP’13 (next talk!) We focus on dynamic, scientific workloads We use an utility function that combines slowdown and utilization Modern portfolio theory in finance Markowitz’52, Magill and Constantinides’76, Black and Scholes’76 Dynamic problem set vs fixed problem set Different workloads and utility functions Selection and Application very different Historical simulation General scheduling

23 23 PDS Group, TUD Agenda 1.Why portfolio scheduling? 2.What is portfolio scheduling? In a nutshell… 3.Our periodic portfolio scheduler for the data center 4.Experimental results 5.Our ongoing work on portfolio scheduling 6.How novel is our portfolio scheduler? A comparison with related work 7.Conclusion

24 24 PDS Group, TUD Conclusion Take-Home Message 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 - http://www.st.ewi.tudelft.nl/~iosup/ - http://www.pds.ewi.tudelft.nl/http://www.st.ewi.tudelft.nl/~iosup/http://www.pds.ewi.tudelft.nl/ - A.Iosup@tudelft.nl - DengKefeng@nudt.eduA.Iosup@tudelft.nlDengKefeng@nudt.edu Alexandru Iosup

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