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Energy Consumption of TAGS in a Heterogeneous Environment under Unknown Service Demand

Lookup NU author(s): Ali Alssaiari, Dr Nigel Thomas

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).

Abstract

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.

Publication metadata

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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