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Intelligent Management of Virtualized Resources for Database Systems in Cloud Environment

Intelligent Management of Virtualized Resources for Database Systems in Cloud Environment. Pengcheng Xiong (Georgia Tech); Yun Chi (NEC Labs America); Shenghuo Zhu (NEC Labs America); Hyun Moon (NEC Labs America); Calton Pu (Georgia Tech); Hakan Hacigumus (NEC Labs America). Overview.

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Intelligent Management of Virtualized Resources for Database Systems in Cloud Environment

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  1. Intelligent Management of Virtualized Resources for Database Systems in Cloud Environment PengchengXiong (Georgia Tech); Yun Chi (NEC Labs America); Shenghuo Zhu (NEC Labs America); Hyun Moon (NEC Labs America); CaltonPu (Georgia Tech); HakanHacigumus (NEC Labs America)

  2. Overview Motivation Background System modeling Resource Allocation Conclusions

  3. Overview Motivation Background System modeling Resource Allocation Conclusions

  4. Hosting database management systems Common practice: One-to-one mapping of DBMSs to nodes

  5. Problem: Poor utilization Both nodes are utilized under 10% most of the time. (2) The maximum CPU usage is much higher than the average CPU usage. PradeepPadala et al, Adaptive control of virtualized resources in utility computing environments(EuroSys '07)

  6. Solution: consolidation based on virtualization

  7. Solution: consolidation based on virtualization 3 CPUs 2 CPUs 4 is enough!

  8. Problem: Provisioning under variable workload

  9. Solution: Adaptive provisioning under variable workload

  10. Goals • Good performance • Maintain SLA • Service differentiation • Good resource utilization

  11. Overview Motivation Background System modeling Resource Allocation Conclusions

  12. MySQLMaster MySQLSlave VM 1 VM 3 VM 2 VM 4 MySQLMaster MySQLSlave Background Gold Client Gold Client Silver Client Virtualized Server I Virtualized Server II Silver Client

  13. What are we controlling ? Goals SLA penalty cost =>Good performance Infrastructure cost=>Good utilization Action cost +) ------------------------------------ Total cost Minimized? NO Set CPU/Memory shares Inner level controller Set number of replicas Outer level controller

  14. Related work • Existing research • Resource allocation & scheduling • Service differentiation • Our contribution: • Non-linear modelof DBMS behavior • Two level controller • High resource utilization • Low query response time, less SLA penalty cost, Service differentiation • Action cost

  15. System overview

  16. Overview Motivation Background System modeling Resource Allocation Conclusions

  17. System modeling • How is the average SLA penalty cost correlated with the system configuration? • Statistic analysis, draw marginal distribution • How can we accurately predict the average SLA penalty cost? • Machine learning techniques, linear and non-linear models

  18. System modeling: statistical analysis Looks like straight line Non-straight line

  19. System modeling: machine learning

  20. Overview Motivation Background System modeling Resource Allocation Conclusions

  21. Resource allocation: two level controller • Find the next direction which can minimize the total cost • Performance cost (related to SLA) • Infrastructure cost (related to replicas) • Action cost

  22. Evaluation • Experiment Environment • MySQL v5.0, MySQL replication • Xen hypervisor • Workload Generator • Dynamic arrival rate follows Poisson distribution • TPC-W Ordering (the browsing requests and the ordering requests are 50%, respectively.) • TPC-W 100 EBs, 10K items. The whole database size is about 280MB.

  23. Baseline The total SLA penalty cost is 2802

  24. Inner Level Controller The total SLA penalty cost is 2363

  25. Inner Level Controller Total SLA penalty cost under different number of replicas

  26. Outer level controller without action cost <-EC2, Small instance, $0.085 per hour

  27. Action cost modeling

  28. Amortization factor Factor=0:the action cost will be distributed along the infinity intervals. (optimistic) Factor=1:the action cost can be compensated in the next interval. (pessimistic) Factor between (0,1):the action cost can be compensated in several intervals.

  29. Outer level controller with action cost

  30. Conclusion • Virtual resource management for database management systems in Cloud computing • Good performance • Maintain SLA • Service differentiation • Good utilization • SmartSLA • Non-linear model of DBMS behavior • Two level controller which takes in to consideration of SLA penalty cost, infrastructure cost and also action cost.

  31. Thank you! ELBA project page: http://www.cc.gatech.edu/systems/projects/Elba/index.html E-mail: xiong@gatech.edu

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