An Efficient Heuristic to Solve a Multi-objective Scheduling Problem in a Cloud Environment

DOI

10.2991/aisr.k.220201.001How to use a DOI?

Keywords

Cloud Computing; Scheduling Problem; Genetic Algorithm; makespan; energy consumption

Abstract

Due to the spread usage of cloud computing and the dramatic increase on resources used by cloud providers such as the data centers, the effect of those resources becomes a major concern of many organizations. Since the huge amount of energy used in those data centers has noticed impact on the world. And by the years the usage is increasing dramatically.

On other hand, cloud providers try to not only minimize the energy consumption but also focus on satisfying customers by completing their requests as soon as possible. To solve this problem, providers use scheduling algorithms to get good balance between makespan (the ending time of the last processed job) and energy consumed in cloud when scheduling tasks to the available machines.

The main contribution of this study is to implement a multi objective genetic algorithm to solve a scheduling problem in a cloud environment (MOGAC) that aims to reduce the makespan and the energy consumption was proposed. The algorithm was implemented using Java and was compared to energy aware min-min algorithm earlier proposed in the literature. The proposed algorithm achieves up to 17% and 0.47% reduction in makespan and energy consumption, respectively, in many situations.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Fatima Hassan
AU  - Jihene Kaabi
PY  - 2022
DA  - 2022/02/02
TI  - An Efficient Heuristic to Solve a Multi-objective Scheduling Problem in a Cloud Environment
BT  - Proceedings of the 2nd International Conference on Industry 4.0 and Artificial Intelligence (ICIAI 2021)
PB  - Atlantis Press
SP  - 1
EP  - 5
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.220201.001
DO  - 10.2991/aisr.k.220201.001
ID  - Hassan2022
ER  -