Energy-efficient tailoring of VM size and tasks in cloud data centers

Nowadays, cloud computing has emerged as the most popular computing platform due to its ability to reduce the operating costs. A sizable chunk of the operating costs is composed of power consumption of the cloud data center alone. It has been observed that improperly sized virtual machines result in comparatively higher power consumption. Although efforts have been made to address the issue of high power consumption, still efficient resource allocation in a cloud environment continues to pose a significant challenge. The proposed design is based on the concept of clustering tasks based on computing resource requirements, and then distributing the tasks among appropriately sized virtual machines on the basis of computing resources. The current paper evaluates the proposed model against representational techniques in the field. A benchmark dataset from Google cloud trace is used for the evaluation. Empirical results analysis has shown that the proposed model offers significant advantages over the existing schemes in so far as load balancing of active physical servers is concerned in terms of defined performance metrics.