Optimization of Energy Consumption in Cloud Computing Datacenters

Abstract

In recent years, cloud computing has emerged as a practical paradigm for providing IT resources, infrastructure and services. This has led to the establishment of large scale datacenters that have substantial energy demands for their operation. These centers are estimated to have the fastest growing carbon foot print among all information and communication technology sector. This work investigates the optimization of the energy consumption in cloud datacenters by using energy efficient allocation of tasks to resources. The work seeks to develop formal optimization models that minimize the energy consumption of computational resources and evaluates the use of existing optimization solvers in testing these models. Energy consumption of cloud computing datacenters is mainly disbursed by the CPU, memory, disk storage, and network, with the CPU consuming the major portion. Hence, as tasks arrive for processing, these tasks must be scheduled efficiently by the cloud resource allocation mechanism. Here, the scheduling problem is modeled using the Integer Linear Programming (ILP) techniques, where models are formulated with the objective of minimizing the total power consumed by the active and idle cores of the servers’ CPUs while meeting a set of constraints. Next, we use these models to carry out a detailed performance comparison between a selected set of Generic ILP and 0-1 Boolean Satisfiability based solvers in solving the ILP formulations. Simulation work is carried out using datacenters configured following industry-standard servers specifications. Results indicate that the developed models have saved up to 37.9% in energy consumption when compared to common techniques such as Round Robin. Furthermore, results also showed that from our selected set of solvers, generic ILP solvers had superior performance when compared to SAT-based ILP solvers especially as the number of tasks and resources grow in size.