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International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749 . Open Access


Energy-Aware Autonomic Resource Scheduling Framework for Cloud

Energy-Aware Autonomic Resource Scheduling Framework for Cloud

Bhupesh Kumar Dewangan
School of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, India.

Amit Agarwal
School of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, India.

Venkatadri M.
School of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, India.

Ashutosh Pasricha
Schlumberger Pvt. Ltd., New Delhi, India.

DOI https://dx.doi.org/10.33889/IJMEMS.2019.4.1-004

Received on August 24, 2018
  ;
Accepted on September 26, 2018

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.

Keywords- Cost, Cloud computing, Energy, Optimization, Resource scheduling, SLA, QoS.

Citation

Dewangan, B. K., Agarwal, A., M., V., & Pasricha, A. (2019). Energy-Aware Autonomic Resource Scheduling Framework for Cloud. International Journal of Mathematical, Engineering and Management Sciences, 4(1), 41-55. https://dx.doi.org/10.33889/IJMEMS.2019.4.1-004.