Energy Saving In Data Centers

Globally CO2 emissions attributable to Information Technology are on par with those resulting from aviation. Recent growth in cloud service demand has elevated energy efficiency of data centers to a critical area within green computing. Cloud computing represents a backbone of IT services and recently there has been an increase in high-definition multimedia delivery, which has placed new burdens on energy resources. Hardware innovations together with energy-efficient techniques and algorithms are key to controlling power usage in an ever-expanding IT landscape. This special issue contains a number of contributions that show that data center energy efficiency should be addressed from diverse vantage points. © 2017 by the authors. Licensee MDPI, Basel, Switzerland

A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments

This article is posted here with permission from the IEEE - Copyright @ 2010 IEEEIn the real world, many optimization problems are dynamic. This requires an optimization algorithm to not only find the global optimal solution under a specific environment but also to track the trajectory of the changing optima over dynamic environments. To address this requirement, this paper investigates a clustering particle swarm optimizer (PSO) for dynamic optimization problems. This algorithm employs a hierarchical clustering method to locate and track multiple peaks. A fast local search method is also introduced to search optimal solutions in a promising subregion found by the clustering method. Experimental study is conducted based on the moving peaks benchmark to test the performance of the clustering PSO in comparison with several state-of-the-art algorithms from the literature. The experimental results show the efficiency of the clustering PSO for locating and tracking multiple optima in dynamic environments in comparison with other particle swarm optimization models based on the multiswarm method.This work was supported by the Engineering and Physical Sciences Research Council of U.K., under Grant EP/E060722/1

On the relationship of degree of separability with depth of evolution in decomposition for cooperative coevolution

Problem decomposition determines how subcomponents are created that have a vital role in the performance of cooperative coevolution. Cooperative coevolution naturally appeals to fully separable problems that have low interaction amongst subcomponents. The interaction amongst subcomponents is defined by the degree of separability. Typically, in cooperative coevolution, each subcomponent is implemented as a sub-population that is evolved in a round-robin fashion for a specified depth of evolution. This paper examines the relationship between the depth of evolution and degree of separability for different types of global optimisation problems. The results show that the depth of evolution is an important attribute that affects the performance of cooperative coevolution and can be used to ascertain the nature of the problem in terms of the degree of separability