Welcome to the InfoSci Platform
Reference Hub10
Improving Energy Efficiency in Web Services: An Agent-Based Approach for Service Selection and Dynamic Speed Scaling

Improving Energy Efficiency in Web Services: An Agent-Based Approach for Service Selection and Dynamic Speed Scaling

Jiwei Huang, Chuang Lin
Copyright: © 2013 |Volume: 10 |Issue: 1 |Pages: 24
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781466631373|DOI: 10.4018/jwsr.2013010102
Cite Article Cite Article

MLA

Huang, Jiwei, and Chuang Lin. "Improving Energy Efficiency in Web Services: An Agent-Based Approach for Service Selection and Dynamic Speed Scaling." IJWSR vol.10, no.1 2013: pp.29-52. http://doi.org/10.4018/jwsr.2013010102

APA

Huang, J. & Lin, C. (2013). Improving Energy Efficiency in Web Services: An Agent-Based Approach for Service Selection and Dynamic Speed Scaling. International Journal of Web Services Research (IJWSR), 10(1), 29-52. http://doi.org/10.4018/jwsr.2013010102

Chicago

Huang, Jiwei, and Chuang Lin. "Improving Energy Efficiency in Web Services: An Agent-Based Approach for Service Selection and Dynamic Speed Scaling," International Journal of Web Services Research (IJWSR) 10, no.1: 29-52. http://doi.org/10.4018/jwsr.2013010102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

With the rapid increase of the energy consumption associated with IT systems and services, energy efficiency is becoming a critical issue in the design, development and management of web service systems. One of the main mechanisms that can be used to reduce the energy consumption is dynamic speed scaling which scales the frequencies of the processors of web servers at hardware level. Another approach is service selection to facilitate the use of energy through effective distribution and management of the web services. In this paper, both the web service selection and server dynamic speed scaling are optimized by maximizing the quality of service (QoS) revenue and minimizing energy costs. Stochastic models of web service systems are proposed, and techniques for quantitative analysis of the performance and energy consumption are investigated. The authors formulate the service selection and speed scaling as a Markov Decision problem, and introduce related algorithms to solve it. Furthermore, the authors build up an optimization framework using multi-agent techniques, and design efficient algorithms to solve the problem in large-scale web service systems. Finally, the effectiveness of their approach is validated by simulation experiments.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.