Job Classification in Cloud Computing: The Classification Effects on Energy Efficiency

Abstract

One of the recent and major challenges in cloud computing is to enhance the energy efficiency in cloud data centers. Such enhancements can be done by improving the resource allocation and management algorithms. In this paper, a model that identifies common patterns for the jobs submitted to the cloud is proposed. This model is able to predict the type of the job submitted, and accordingly, the set of users' jobs is classified into four subsets. Each subset contains jobs that have similar requirements. In addition to the jobs' common pattern and requirements, the users' history is considered in the jobs' type prediction model. The goal of job classification is to find a way to propose useful strategy that helps improve energy efficiency. Following the process of jobs' classification, the best fit virtual machine is allocated to each job. Then, the virtual machines are placed to the physical machines according to a novel strategy called Mixed Type Placement strategy. The core idea of the proposed strategy is to place virtual machines of the jobs of different types in the same physical machine whenever possible, based on Knapsack Problem. This is because different types of jobs do not intensively use the same compute or storage resources in the physical machine. This strategy reduces the number of active physical machines which leads to major reduction in the total energy consumption in the data center. A simulation of the results shows that the presented strategy outperforms both Genetic Algorithm and Round Robin from an energy efficiency perspective.