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Lookup NU author(s): Ali Alssaiari, Dr Nigel Thomas
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
This paper models the task assignment based on guessing size (TAGS) job allocation algorithm using Markovian processing algebra; PEPA. It aims to analyse performance and energy consumption. The working environment is assumed to be heterogeneous, and the job size distribution is assumed to be a two phase hyper-exponential. Furthermore, the queues are bounded. A two nodes system is implemented with exponentially distributed incoming tasks. We analysed the performance metrics and energy consumption under different arrival rates. We found TAGS can perform well and improve performance, although it increases total energy consumption. Finally, we calculated the energy per job to evaluate TAGS in a heterogeneous environment, and demonstrated that TAGS reduces energy consumption per job when the system is under a heavy load.
Author(s): Alssaiari A, Thomas N
Publication type: Article
Publication status: Published
Journal: Sustainable Computing: Informatics and Systems
Year: 2021
Volume: 30
Print publication date: 01/06/2021
Online publication date: 08/04/2021
Acceptance date: 04/04/2021
Date deposited: 31/01/2022
ISSN (electronic): 2210-5379
Publisher: Elsevier
URL: https://doi.org/10.1016/j.suscom.2021.100557
DOI: 10.1016/j.suscom.2021.100557
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