Energy-Aware Autonomic Resource Scheduling Framework for Cloud

Bhupesh Kumar Dewangan,Amit Agarwal,Venkatadri M,Ashutosh Pasricha

doi:10.33889/ijmems.2019.4.1-004

Abstract

Cloud computing is a platform where services are provided through the internet either free of cost or rent basis. Many cloud service providers (CSP) offer cloud services on the rental basis. Due to increasing demand for cloud services, the existing infrastructure needs to be scale. However, the scaling comes at the cost of heavy energy consumption due to the inclusion of a number of data centers, and servers. The extraneous power consumption affects the operating costs, which in turn, affects its users. In addition, CO2 emissions affect the environment as well. Moreover, inadequate allocation of resources like servers, data centers, and virtual machines increases operational costs. This may ultimately lead to customer distraction from the cloud service. In all, an optimal usage of the resources is required. This paper proposes to calculate different multi-objective functions to find the optimal solution for resource utilization and their allocation through an improved Antlion (ALO) algorithm. The proposed method simulated in cloudsim environments, and compute energy consumption for different workloads quantity and it increases the performance of different multi-objectives functions to maximize the resource utilization. It compared with existing frameworks and experiment results shows that the proposed framework performs utmost.

Highlights

Optimization of resources in the cloud is a set of process, which maximize the utilization of the available resources (Preethi et al, 2014)
If available resources are well utilized through the optimization process, execution time and the cost will be minimized, and though this the power consumption can be optimized
This paper proposed an answer for a disconnected application position issue, to be specific Application Component Placement (ACP) where the area of the information part is thinking about as one of the central factors and consider as preparing correspondence and capacity necessities

Summary

Introduction

Optimization of resources in the cloud is a set of process, which maximize the utilization of the available resources (Preethi et al, 2014). This technique improvises the efficiency of the system by consideration of multi-objective functions. If available resources are well utilized through the optimization process, execution time and the cost will be minimized, and though this the power consumption can be optimized. Self-optimization is the process to improvise the performance of the system by adopting intelligence techniques. It automatically finds the optimal solution for resource provisioning

Methods

Results

Conclusion