Designing Energy Management Aware Task Scheduling Algorithm And Model For Cloud Data Centers

Abstract

In cloud computing many tasks come to servers from different sites for computation and storage which makes data centers consume large energy and become less responsive. Task scheduling is one of the best ways to utilize cloud data center resources efficiently, lower power consumption and latency, and empowers available bandwidth use. Task scheduling algorithms and data center network models employed previously are not much efficient from energy consumption perspective due to the features that they haven’t included. To solve this problem an appropriate and efficient proximity based task scheduling technique and model are designed and implemented for cloud computing data centers.

This research work proposes and implements a new proximity based task scheduling algorithm and scalable data center model that can minimize power consumption and increase scalability of cloud computing data centers. The evaluation results, found from the experiment are presented and showed more efficient outcome in terms of energy efficiency for both the proposed scheduling algorithm and data center network model.