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Multifaceted Resource Management in Virtualized Providers

Multifaceted Resource Management in Virtualized Providers

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Multifaceted Resource Management in Virtualized Providers

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  1. MultifacetedResource Management in VirtualizedProviders Íñigo Goiri PhDDefense June 14th, 2011 Advisors: Jordi Guitart and Jordi Torres

  2. Motivation Internet BookStore Companiesoffertheirservices overthe Internet

  3. ServiceProvidersoverthe Internet Number of users increases Internet BookStore Providerrequires more resources Load increases

  4. ServiceProvidersoverthe Internet Number of users decreases Internet BookStore Providerrequiresfewerresources

  5. VirtualizationonServiceProviders Internet BookStore Encapsulatetasks in Virtual Machines Load isunbalanced Database Application Server Database Application Server VM VM VM VM VM VM VM VM Database Web Server Web Server Web Server Application Server Application Server WebServer WebServer WebServer DDBB DDBB DDBB

  6. Providing Virtual Machines WebServer Internet BookStore Virtualized Provider Virtualized Provider HPC Desktop VM VM VM VM VM VM VM VM Provider offers its idle Infrastructure as a Service (IaaS) We can also offer applications encapsulated in VMs

  7. ManagingVirtualizedProvider’sResources Balanced load VMs get enough resources Energy consumption is high

  8. ManagingVirtualizedProvider’sResources Unbalanced load Some VMs don’t get enough resources Energy consumption is lower

  9. ManagingVirtualizedProvider’sResources Challenge EfficientlyManageVirtualizedProvider’sResources MaximizeProvider’sProfit Every VM gets enough resources Energy consumption is lower

  10. Contents • Motivation • Multifaceted Scheduling • Cost-Benefit Model • Scheduling Policy • Evaluation • Multiprovider Scheduling • Capacity Planning • Scheduling • Evaluation • Conclusion

  11. Multifaceted Scheduling • Cost-benefit model • Multiple facets to consider • Aggregate facets into costs • Consider impact of facets on other facets • Scheduling policy • Maximize provider profit • Evaluation

  12. MultipleFacetstoConsider • Service Level Agreement • Virtualization Management • Energy Consumption • Infrastructure Cost

  13. 1. Service Level Agreement • Contract between user and provider • User pays for resources • Pay as you go: SLARevenue(t(VM)) • If the provider does not provide the QoS • SLA Penalty: SLAPenalty(VM) Virtualized Provider Revenue Penalties

  14. 1. Service Level AgreementSupported Applications • Batch • Deadlines • Example • HPC jobs • Service • Uptime • Performance • Example • Web Servers Task 1 Task 2 Task 3 Response Time Time Time

  15. 1. Service Level AgreementResource Heterogeneity • Xeon processor • High energy consumption • High performance • Atom processor • Low energy consumption • Low performance Task A Task A Task B Task B Task C Task C Max Max 0 0 Wh Wh

  16. 1. Service Level Agreement • Estimate SLA Penalties • Actions may imply future violations • SLAPenalty’(Host, VM) • Factors that might provoke violations • Runtime overhead • Slow host • High host utilization • Other facets evaluate penalty estimations

  17. 2. Virtualization Management • Overhead to manage Virtual Machines • Start VMs • Migrate VMs between nodes Time Time

  18. 2. Virtualization Management • Overhead to manage Virtual Machines • Start VMs • Migrate VMs between nodes Migration Off Off Migration Time

  19. 2. Virtualization Management • Overhead implies • Extra time to the VM • Extra load to the Host • It can imply SLA penalties • Violate deadline • No enough resources to provide performance • Estimate SLA penalty for every action (Time, Load) → SLAPenalty’(Host, VM) → €

  20. 3. Energy ConsumptionEnergy vs. SLA • Low Consolidation • Consume a lot of energy • Fulfill SLA • High Consolidation • Save energy • Violate SLA

  21. 3. Energy Consumption vs. SLABatch Type • Low Consolidation • High Consolidation Time Off Max Max Max Max 0 0 0 0 Wh Wh Wh Wh Time Time

  22. 3. Energy Consumption vs. SLAService Type • Low Consolidation • High Consolidation Off Max Max Max Max 0 0 0 0 Wh Wh Wh Wh Time Time Time

  23. 3. Energy Consumption • Energy cost Wh → € • SLA penalties Host Utilization→ SLAPenalty’(Host, VM) → €

  24. 4. Infrastructure Cost • Provider owns infrastructure • Servers • Air conditioners • Racks… • Capital Expense (CAPEX) • Cost is amortized over time • Provider has already paid for the hardware €/Period

  25. Multiple Facets to ConsiderCalculate profit of VM at Host • Service Level Agreement: SLARevenue(VM) +€ • Virtualization Management: Time: SLAPenalty’(VM) -€ Load: SLAPenalty’(VMs in Host)due to VM -€ • Energy Consumption: Energy consumed by VM at Host -€ SLA Penalty’(VMs in Host) -€ • Infrastructure Cost: Cost of Host running VM -€ Total Profit €

  26. Scheduling Policy • Decide best VM placement • Maximize provider profit • Hill Climbing (Greedy) • When to schedule? • System changes • Periodically • Model Virtualized Provider as a matrix • VM x Host cells • Each is profit of placing VM in Host • Use cost-benefit model

  27. Scheduling Policy Queue: 6 7 1 2 A OFF B C 3 4 5

  28. Scheduling Policy 1 2 3 4 5 6 7 1.5€ 1.5€ 1.2€ 1.2€ 0.7€ 0.7€ -0.2€ 1.2€ -0.3€ -0.3€ 0.2€ 0.2€ -∞ -∞ A 1.5€ 1.2€ 0.7€ 1.2€ -0.3€ 0.2€ -∞ After multiple iterations… B -∞ -∞ -∞ 0.2€ 1.3€ -0.5€ -0.5€ 0.1€ 0.1€ 0.3€ 0.3€ -∞ -∞ -∞ -∞ -∞ 1.3€ -0.5€ 0.1€ 0.3€ -∞ -∞ -0.5€ -0.5€ -∞ 0.2€ 0.7€ -1.5€ 1.2€ -0.1€ -0.1€ -0.7€ -0.7€ 0.2€ 0.2€ C Recalculate cost of allocating Every VM at every Host Calculate cost of allocating Every VM at every Host Schedule VM in maximum Profit placement When cost is minimized Dispatch VMs -∞ -0.5€ 0.2€ -1.5€ -0.1€ -0.7€ 0.2€

  29. Scheduling Policy Queue: 6 7 1 2 A OFF B C 3 4 5

  30. EvaluationMultifaceted Scheduling • One week heterogeneous workload • Batch: Grid5000 • Service: SPECWeb2005 • SLA metrics • Batch: Deadline (Added +20% Base Runtime) • Service: Performance (Response Time) • Provider with 65 nodes • Enough to satisfy workload peaks

  31. EvaluationScheduling Policies • Backfilling + Migration • Backfill VMs • Migrate to consolidate • Perfect SLA • Analytical (NP) • Perfect SLA fulfillment • Optimal energy consumption • Our proposal • Backfilling + Migration • Aggregate multiple facets • Uses cost-benefit model • Maximize profit 1 3 2 4 OFF 2 1 4 3 OFF

  32. Evaluation Energy consumption SLA fulfillment + ← Consolidation → - + ← Consolidation → -

  33. Evaluation

  34. Evaluation • Power consumption over time

  35. Multifaceted SchedulingLimitations • Service Level Agreement • Virtualization Management • Energy Consumption • Infrastructure Cost • Fixed costs

  36. Contents • Motivation • Multifaceted Scheduling • Cost-Benefit Model • Scheduling Policy • Evaluation • Multiprovider Scheduling • Capacity Planning • Scheduling • Evaluation • Conclusion

  37. Multiprovider SchedulingOutsourcing • Infrastructure cost • Capital Expenses (CAPEX) Solution: CAPEX → OPEX External Provider

  38. Multiprovider SchedulingOutsourcing • Reduce provider infrastructure (CAPEX) • Multifaceted Scheduling + Outsourcing • Add outsourcing cost (OPEX) • Slower VM creation • Limited VM management External Provider

  39. EvaluationOutsourcing • Add outsourcing to “Multifaceted Scheduling” • Same environment • Reduce local resources: 65 → 20 → 0 nodes • 20 nodes is enough to provide the average • External provider: EC2 US • 0.085 €/hour • 5 minutes to start a VM

  40. EvaluationOutsourcing

  41. Multiprovider SchedulingFederation • How many local resources? • Optimal number of resources • Change capacity planning • How to schedule? • New actuators • New trade-offs • Characterize provider profitability • Cost-benefit model

  42. Federated Provider Model • Multidimensional problem • Evaluate provider profile • Provider capabilities • Expected workload • VM pricing • Evaluate costs • CAPEX: Infrastructure • OPEX: Energy, Cooling,…

  43. Multiprovider SchedulingCharacterize federation Leverage federated provider model for: Phase 0. Capacity planning • Provider building and setup process • Decide optimal number of nodes Phase 1. Scheduling • Online process • Decide actions to take

  44. Phase 0. Capacity Planning • Load is variable over time • If infrastructure costs are fixed • Underprovision: Cannot support peaks • Overprovision: Underutilized resources

  45. Phase 0. Capacity Planning Underprovision • Solution: Outsourcing • Send peaks to other providers • Reduce provider infrastructure costs • Pay for using external resources External Provider

  46. Phase 0. Capacity Planning Overprovision • Solution: Insourcing • Offer idle resources to other providers • Cheaper price • Enough resources to support peaks Offer to other Providers

  47. Phase 1. Scheduling • Analyze provider profitability • Decide best actions to perform • New actuators to consider • Outsourcing • Insourcing • Old actuators change • Turn on/off nodes vs. Insourcing

  48. EvaluationCharacterization • Providerprofitability (darkerisbetter) • Offering 80% of the idle resources • Amazon EC2 pricing No Insourcing Insourcing

  49. EvaluationPhase 0. CapacityPlanning • ISP workload over a week • Get optimal capacity • Leverage provider model • Revenue > Costs Overprovision 100 nodes Undeprovision

  50. EvaluationPhase 1. Scheduling