Efficient task optimization algorithm for green computing in cloud.

G, Thanmayatejaswi; Ch, Dileep Chakravarthy; Varma, G.P.S.; Mekala, M.S.

Abstract

Cloud infrastructure assets are accessed by all hooked heterogeneous network servers and applications to maintain entail reliability towards global subscribers with high performance and low cost is a tedious challenging task. Most of the extant techniques are considered limited constraints like task deadline, which leads Service Level Agreement (SLA) violation. In this manuscript, we develop Hadoop based Task Scheduling (HTS) algorithm which considers a task deadline time, completion time, migration time and future resource availability of each virtual machine. The Intelligent System (IS) enabled with adaptive neural computation method to assess all above attributes. Specifically, the result of Prophecy Resource Availability (PRA) method has been used to assess the status of each Virtual Machine (VM), which helps to streamline the resource wastage and increases the response time with low SLA violation rate.

Citation

G, T., CH, D.C., VARMA, G.P.S. and MEKALA, M.S. 2023. Efficient task optimization algorithm for green computing in cloud. Internet technology letters [online] 6(1): ubiquitous clouds and social Internet of Things, article e254. Available from: https://doi.org/10.1002/itl2.254

• Journal Article Type: Article
• Acceptance Date: Oct 21, 2020
• Online Publication Date: Nov 11, 2020
• Publication Date: Feb 28, 2023
• Deposit Date: Feb 27, 2023
• Publicly Available Date: Feb 27, 2023
• Journal: Internet technology letters
• Electronic ISSN: 2476-1508
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 6
• Issue: 1
• Article Number: e254
• DOI: https://doi.org/10.1002/itl2.254
• Keywords: Cloud computing; Energy optimization; Hadoop system; Measurement decision system
• Public URL: https://rgu-repository.worktribe.com/output/1867323