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Efficient task optimization algorithm for green computing in cloud.

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

Authors

Thanmayatejaswi G

Dileep Chakravarthy Ch

G.P.S. Varma



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 Mar 28, 2024
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

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