Performance and Power Management for Cloud Infrastructures

1. Performance and Power Managementfor Cloud Infrastructures • Hien Nguyen Van; Tran, F.D.; Menaud, J.-M. • Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on

2. Introduction • From the cloud operator perspective, the key issue is to maximize profits by minimizing the operational costs of the datacenter and the SLA violations of hosted applications. • Power management in cloud computing datacenters is becoming a crucial issue since it dominates its operational costs.

3. Introduction • A dynamic resource provisioning system is needed capable of addressing two main issues: • How much resource (CPU, memory. . . ) to allocate to hosted applications? • Where to place the application workloads within the datacenter to maximize energy savings?

4. Introduction • The main contributions of this paper are: • Utility-based dynamic Virtual Machine (VM) provisioning manager capable of balancing application SLA compliance with energy consumption • Dynamic VM placement manager which consolidates VMs on the minimum number of physical hosts through VM live migration so that idle hosts can be turned off to save energy • Two-level resource management middleware framework with a clear separation between application-specific management and a generic resource management substrate.

5. Related work

6. Resource management system • We consider a Cloud Computing datacenter DC composed of n virtualized physical hosts DC = {H1, . . . ,Hn}. • CPU Capacity = CPU(H1) • Memory capacity = Mem(H1) • A set of m applications A = {A1, . . . ,Am} are hosted on this virtualized infrastructure. • Each Host corresponds to a fixed CPU capacity (MHz) spread over a given number of virtual CPUs and a given memory size.

7. Utility-directed VM provisioning • A VM allocation matrix solution must meet the capacity constraints of the datacenter:

8. VM placement • The placement solution must satisfy the capacity constraints of the physical hosts: • The goal is to maximize the number of idle physical hosts Nidlewhich can be turned off:

9. Middleware Framework

10. Middleware Framework • Performance model component which performs the mapping between resource capacity (expressed in number of VMs), workload and QoS • Utility function component which encapsulates the application-specific utility function • Application scaler component which hides the application-specific mechanism used to scale up or down horizontally the application.

11. Evaluation

12. Evaluation

13. Evaluation

14. Conclusion • In this paper we have addressed the problem: • Resourceallocation in Cloud infrastructures • Application performance and energy cost while providing • Cloud administrator high-level knobs to control the resource • Management system with regard to application-level SLAs • Datacenter exploitation costs

15. Reference • Hien Nguyen Van; Tran, F.D.; Menaud, J.-M.; , "Performance and Power Management for Cloud Infrastructures," Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on , vol., no., pp.329-336, 5-10 July 2010

16. Thank you!