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For a long time, the development of computing systems has been focused on the improvement of the performance. This objective leads to the appearance of more efficient system design and increasing density of the components described as Moore’s law (Moore 1998). Meanwhile, however, it is reported that the average power consumption per server class is increasing every year (Koomey, 2007), and the problem of energy consumption is even worse for large-scale computing infrastructures such as web service systems, cloud systems and data centers (Beloglazov, Buyya et al., 2011). It was estimated that it took about 61 billion kWh for the total electricity and cost about 4.5 billion dollars for running IT infrastructures in the US in 2006 (Brown, Masanet et al., 2007). In 2009, Google reported that 0.0003 kWh of energy was consumed per Google search, resulting in more than 32 million kWh being consumed per year (Hölzle, 2009). Therefore, besides high performance, energy efficiency has become an important consideration during the design and development of IT systems. Recently, more and more attention has been paid to power consumption and energy efficiency in computer system design and optimization.
A Web service is a software system designed to support interoperable machine-to-machine interaction over a network (Hass & Brown, n.d.). As web services and services computing become more and more popular, with the growing popularity of third-party commodity clusters and cloud environments, there is a significant opportunity promoting energy efficiency through effective distribution and management of geographically-dispersed web services (Bartalos & Blake, 2012). It helps facilitate effectiveness of the use of national resources and make more profits to the service providers. Therefore, the energy efficiency in web services and cloud computing has become a hot topic in both academia and industry.
In web service systems, there are many categories of services, each of which may have multiple instanced services deployed on different web servers. An effective way to achieve high energy efficiency is the efficient service allocation or selection at service level which makes use of distributed services placed in different servers (Liu, Quan et al., 2011). Choosing the appropriate services in different physical servers can help improve the performance of services as well as reduce power consumption of physical servers, especially when the servers are in sleeping or stand-by states before the service placement. Another way is to dynamically adjust the CPU frequencies of the servers at hardware level to effectively reduce power consumption in web servers (Beloglazov, Buyya et al., 2011). Each of the two methods can be formulated as an optimization problem, and has been studied separately in related literature. However, the combination of the two problems, which can help further improve the energy efficiency of the whole web service systems, is unexplored.
In this work, we fill this gap, and jointly study the service selection and dynamic server speed scaling in web service systems to achieve high energy efficiency while meeting the performance requirements. We propose stochastic models to describe the web service systems, and give mathematical analysis and related updating techniques of them to evaluate the energy efficiency. Then we formulate both the service selection problem and dynamic speed scaling problem using Markov Decision Process (MDP) and introduce two relevant algorithms to solve them. We theoretically prove that the MDP can optimize the steady-state rewards in the stochastic models. Furthermore, we design a framework and related algorithms to solve the problem in large-scale web service systems effectively and efficiently using multi-agent techniques. The simulation results show that our proposed approach can significantly and dynamically reduce the energy consumption of the web service systems.
Our main contribution is as follows: