1. Copyright © 2010, MBL@CS.NCTU Performance and Power Management for Cloud Infrastructures Hien Nguyen Van; Tran, F.D.; Menaud, J.-M. Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on

2. Copyright © 2010, MBL@CS.NCTU 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. Copyright © 2010, MBL@CS.NCTU 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. Copyright © 2010, MBL@CS.NCTU 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. Copyright © 2010, MBL@CS.NCTU Related work Issues Performance and Power Management for Cloud Infrastructures Our proposed method SLAYES Monitor applications resource YES Resource allocationYES Prediction require resourcesNOYES Different sizes of VM capacity NOYES

6. Copyright © 2010, MBL@CS.NCTU Resource management system We consider a Cloud Computing datacenter DC composed of n virtualized physical hosts DC = {H 1,...,H n }. –CPU Capacity = CPU(H 1 ) –Memory capacity = Mem(H 1 ) A set of m applications A = {A 1,...,A m } 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. Copyright © 2010, MBL@CS.NCTU Utility-directed VM provisioning A VM allocation matrix solution must meet the capacity constraints of the datacenter:

8. Copyright © 2010, MBL@CS.NCTU 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 N idle which can be turned off:

9. Copyright © 2010, MBL@CS.NCTU Middleware Framework

10. Copyright © 2010, MBL@CS.NCTU 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. Copyright © 2010, MBL@CS.NCTU Evaluation

12. Copyright © 2010, MBL@CS.NCTU Evaluation

13. Copyright © 2010, MBL@CS.NCTU Evaluation

14. Copyright © 2010, MBL@CS.NCTU Conclusion In this paper we have addressed the problem: –Resource allocation 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. Copyright © 2010, MBL@CS.NCTU 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. Copyright © 2010, MBL@CS.NCTU Thank you!