Cart (Loading....) | Create Account
Close category search window
 

Analysis and optimization of service availability in a HA cluster with load-dependent machine availability

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Chee-Wei Ang ; Inst. for Infocomm. Res., Singapore, Singapore ; Chen-Khong Tham

Calculations of service availability of a high-availability (HA) cluster are usually based on the assumption of load- independent machine availabilities. In this paper, we study the issues and show how the service availabilities can be calculated under the assumption that machine availabilities are load dependent. We present a Markov chain analysis to derive the steady-state service availabilities of a load-dependent machine availability HA cluster. We show that with a load-dependent machine availability, the attained service availability is now policy dependent. After formulating the problem as a Markov decision process, we proceed to determine the optimal policy to achieve the maximum service availabilities by using the method of policy iteration. Two greedy assignment algorithms are studied: least load and first derivative length (FDL) based, where least load corresponds to some load balancing algorithms. We carry out the analysis and simulations on two cases of load profiles: In the first profile, a single machine has the capacity to host all services in the HA cluster; in the second profile, a single machine does not have enough capacity to host all services. We show that the service availabilities achieved under the first load profile are the same, whereas the service availabilities achieved under the second load profile are different. Since the service availabilities achieved are different in the second load profile, we proceed to investigate how the distribution of service availabilities across the services can be controlled by adjusting the rewards vector.

Published in:

Parallel and Distributed Systems, IEEE Transactions on  (Volume:18 ,  Issue: 9 )

Date of Publication:

Sept. 2007

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.