An Efficient Approach for Energy Conservation in Cloud Computing Environment

TL;DR

This paper introduces a new task scheduling algorithm for cloud computing that explicitly considers multiple resource types to improve energy efficiency, demonstrating better performance and lower energy consumption than existing methods.

Contribution

The paper presents a novel resource-aware task scheduling algorithm that enhances energy efficiency in cloud environments by explicitly optimizing CPU, disk, and I/O utilization.

Findings

Proposed algorithm reduces energy consumption compared to MaxUtil.

Simulation results show improved resource utilization.

Algorithm is effective across synthetic datasets.

Abstract

Recent trends of technology have explored a numerous applications of cloud services, which require a significant amount of energy. In the present scenario, most of the energy sources are limited and have a greenhouse effect on the environment. Therefore, it is the need of the hour that the energy consumed by the cloud service providers must be reduced and it is a great challenge to the research community to develop energy-efficient algorithms. To design the same, some researchers tried to maximize the average resource utilization, whereas some researchers tried to minimize the makespan. However, they have not considered different types of resources that are present in the physical machines. In this paper, we propose a task scheduling algorithm, which tries to improve utilization of resources (like CPU, disk, I/O) explicitly, which in turn increases the utilization of active resources. For this, the proposed algorithm uses a fitness value, which is a function of CPU, disk and I/O utilization, and processing time of the task. To demonstrate the performance of the proposed algorithm, extensive simulations are performed on both proposed algorithm and existing algorithm MaxUtil using synthetic datasets. From the simulation results, it can be observed that the proposed algorithm is a better energy-efficient algorithm and consumes less energy than the MaxUtil algorithm.