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

Management intelligence for optimal resource allocations in network server systems

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

3 Author(s)
Ravindran, K. ; Dept. of Comput. Sci., City Univ. of New York, New York, NY, USA ; Rabby, M. ; Elmetwaly, S.

In this paper, we provide a control-theoretic treatment of the resource allocations that adaptively occur in a QoS-aware network server system. Here, the target system being controlled is a logical service point that processes the transactions requested by clients using a resource infrastructure, with a goal of maximizing the revenues. Accurate management of resource allocations with a revenue-oriented goal is quite complex, due to the interactions among various transactions that dynamically share the resources in the system (such as server nodes, disks, content caches, and network bandwidth). So, we adopt an on-line monitor-and-control approach, aided by heuristics, that iteratively adjusts the resource allocation based on the observed transaction drop rate. We undertake a case study of end-to-end QoS-adaptive data transfer to illustrate the methodology. In terms of control theory, the bandwidth allocation and the packet loss rate constitute the system input and output respectively, with the heuristics-based bandwidth adjustment strategies incorporated in a controller along the feedback loop. The use of control theory allows offering predictable convergence properties of the QoS seen by applications, while maximizing the service provider revenues.

Published in:

Network Operations and Management Symposium (NOMS), 2010 IEEE

Date of Conference:

19-23 April 2010

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.