Optimizing Power Consumption in Cloud Using Task Consolidation

Energy consumed by modern computer systems, particularly by servers in a Cloud has almost reached at an unacceptable level. Also the energy consumed due to underutilization of resource accounts almost 60% of the energy consumed at peak load [6]. Therefore, task consolidation plays an important role in cloud computing, which map users’ service requests to appropriate resources resulting in proper utilization of various cloud resources. Task Consolidation results in significant improvements in energy savings and also enhances overall performance of cloud computing. In our approach, we present an energy aware model for task consolidation problem. The model includes description of physical hosts, virtual machines and service requests (tasks) submitted by users. For the proposed model, an Energy Aware Task Consolidation (EATC) algorithm is developed. ETC (Expected Time to Compute) matrix is used to generate heterogeneity in the cloud system. Performance is evaluated against another heuristic and the results show significant improvement in energy savings.