Vol.67, No.1, 2021, pp.253-265, doi:10.32604/cmc.2021.013695
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ARTICLE
Optimal Resource Allocation and Quality of Service Prediction in Cloud
  • Priya Baldoss1,2,*, Gnanasekaran Thangavel3
1 Information and Communication Engineering Department, Anna University, Chennai, India
2 Department of Computer Science and Engineering, Sri Sai Ram Engineering College, Chennai, India
3 RMK Engineering College, Chennai, India
* Corresponding Author: Priya Baldoss. Email:
Received 17 August 2020; Accepted 21 October 2020; Issue published 12 January 2021
Abstract
In the present scenario, cloud computing service provides on-request access to a collection of resources available in remote system that can be shared by numerous clients. Resources are in self-administration; consequently, clients can adjust their usage according to their requirements. Resource usage is estimated and clients can pay according to their utilization. In literature, the existing method describes the usage of various hardware assets. Quality of Service (QoS) needs to be considered for ascertaining the schedule and the access of resources. Adhering with the security arrangement, any additional code is forbidden to ensure the usage of resources complying with QoS. Thus, all monitoring must be done from the hypervisor. To overcome the issues, Robust Resource Allocation and Utilization (RRAU) approach is developed for optimizing the management of its cloud resources. The work hosts a numerous virtual assets which could be expected under the circumstances and it enforces a controlled degree of QoS. The asset assignment calculation is heuristic, which is based on experimental evaluations, RRAU approach with J48 prediction model reduces Job Completion Time (JCT) by 4.75 s, Make Span (MS) 6.25, and Monetary Cost (MC) 4.25 for 15, 25, 35 and 45 resources are compared to the conventional methodologies in cloud environment.
Keywords
Cloud computing; resource utilization; robust resource allocation and utilization (RRAU) approach; job completion time; quality of services; monetary cost; make span
Cite This Article
P. Baldoss and G. Thangavel, "Optimal resource allocation and quality of service prediction in cloud," Computers, Materials & Continua, vol. 67, no.1, pp. 253–265, 2021.
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