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Automated Workload Management in Virtualized Data Centers

Automated Workload Management in Virtualized Data Centers. Xiaoyun Zhu Hewlett Packard Laboratories Sigmetrics 2008 Tutorial: Introduction to Control Theory and Its Application to Computing Systems. Outline. Background Next generation data center Server consolidation and virtualization

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Automated Workload Management in Virtualized Data Centers

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  1. Automated Workload Management in Virtualized Data Centers Xiaoyun ZhuHewlett Packard Laboratories Sigmetrics 2008 Tutorial: Introduction to Control Theory and Its Application to Computing Systems

  2. Outline • Background • Next generation data center • Server consolidation and virtualization • Case study • Shared hosting platform • Workload management goals • Problem formulation • Adaptive optimal controller design • Testbed and performance evaluations • Summary SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

  3. accesstier edge routers routing switches authentication, DNS,intrusion detect, VPNweb cache 1st level firewall webtier load balancingswitches web servers internet web page storage(NAS) 2nd level firewall applicationtier switches processingelements switchedfabric storageelements applicationservers files(NAS) switches databasetier databaseSQL servers infrastructure on demand storage areanetwork(SAN) intranet Next generation data centers- the utility computing vision large scale virtualized utility fabric provides application services to millions of users Multi-tiered applications SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

  4. workload resource allocation resource shares measured utilization target utilization error Container Actuator Controller - Server consolidation and virtualization- key technology enablers • Container:encapsulates a share of server resources • CPU, memory, network I/O, disk I/O • Provides performance isolation • Actuator: APIs for dynamic resource allocation to containers • Controller: Workload management tools (e.g., HP WLM) can dynamically size a container to maintain a target utilization SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

  5. app 1 Virtualized Server N Virtualized Server 1 Virtualized Server 2 DBS_1 A WS_1 A AS_1 A QoS sensor app 2 S S S DBS_2 A WS_2 A AS_2 A QoS sensor S S S ● ● ● ● ● ● ● ● ● app M workload M DBS_M A WS_M A AS_M A QoS sensor S S S Case study- a shared hosting platform workload 1 workload 2 resource allocation decisions measured QoS and system metrics Resource Controller application QoS goals, QoS differentiation policy SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

  6. Workload management goals • Meets the quality of service (QoS) goal for every application by providing sufficient resources to each component [ACC’06, FeBID’07] • A predictive controller has been integrated into the HP Global Workload Manager (gWLM) product • Ensures high resource utilization (by providing “just enough” resources so that more applications can be hosted in a given server pool) • Provides service differentiation among co-hosted applications during resource contention • Focus on one type of resource (CPU) • Desired level of differentiation should be maintained in spite of workload changes SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

  7. Output regulation problem • M applications, each has N tiers • u(k): (M-1) x N inputs: • y(k): (M-1) outputs: • Reference: SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

  8. Challenges and our solution • Challenges • No first principle model characterizing the relationship between u and y • The relationship varies with the workload • An adaptive optimal controller SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

  9. Online model estimation Linear input-output model for local approximation: Online adaptation using RLS: SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

  10. Linear quadratic optimal controller • Minimizing quadratic cost function • Optimal solution: SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

  11. Experimental validation • 2 HP Proliant servers (N=2) • 4GB RAM, Gigabit Ethernet • Virtualized using Xen 3.0.3 • Credit-based CPU scheduler • Two Applications (M=2) • RUBiS online auction benchmark • 22 transaction types (browsing, bidding, viewing,…) • Apache Web Server • MySQL Database Server • Use response time (RT) as QoS metric • C1 = 100%, C2 = 40% • Ts = 20 sec SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

  12. Small Overshoot Stability and Accuracy Quick Settling Evaluation results (I) • Performance with varying references • Desired QoS ratio yref = 0.3  0.5  0.7 • WL1 = WL2 = 500 users Achieved application QoS CPU allocation and consumption SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

  13. Evaluation results (II) • Performance with varying workloads • Desired QoS ratio yref = 0.7 • WL1 = 300500 users, WL2 = 500 users Achieved application QoS CPU allocation and consumption SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

  14. Summary • Applied control theory to the design of workload management solutions for virtualized data centers • Evaluated a self-tuning optimal controller on a lab testbed • During resource contention, our controller provides service differentiation to co-hosted applications by automated allocation of shared server resource • The closed-loop system shows good SASO properties as the reference inputs change or as the workloads vary SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu.

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