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.
Service selection in web service compositions optimizing energy consumption and service response time
