copyright © 2010, performance and power management for cloud infrastructures hien nguyen van; tran,...

Copyright © 2010, [email protected]

Performance and Power Managementfor Cloud Infrastructures

Hien Nguyen Van; Tran, F.D.; Menaud, J.-M.

Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on


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.


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?


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.


Related work

IssuesPerformance and Power Managementfor Cloud Infrastructures

Our proposed method

SLA YES YES

Monitor applications resource YES YES

Resource allocation YES YES

Prediction require resources NO YES

Different sizes of VM capacity NO YES


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.


Utility-directed VM provisioning

• A VM allocation matrix solution must meet the capacity constraints of the datacenter:


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 Nidle which can be turned off:


Middleware Framework


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.


Evaluation


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


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


Thank you!

copyright © 2010, performance and power management for cloud infrastructures hien nguyen van; tran,...

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