Energy Efficient Workflow Scheduling in Cloud Computing Systems using Particle Swarm Optimization

DOI

10.2991/978-94-6463-529-4_24How to use a DOI?

Keywords

Workflow Scheduling; PSO; HEFT; Makespan; Energy Consumption

Abstract

This research paper proposes a novel approach for minimizing makespan and energy consumption in workflow scheduling for cloud computing systems using particle swarm optimization (PSO). Workflow scheduling is a critical task in cloud computing, where multiple tasks are assigned to each available virtual machine to ensure the precedence constraint of the workflow application. However, traditional scheduling methods often lead to longer makespan and higher energy consumption, which can negatively impact the overall efficiency and sustainability of the cloud infrastructure. The proposed PSO-based approach optimizes the task allocation and scheduling process by leveraging the swarm intelligence of particles to search for the optimal solution. The PSO algorithm is adapted to consider both makespan and energy consumption as objective functions, allowing for a more comprehensive and balanced optimization approach. The proposed approach is evaluated using a workflow application, and the results show that it outperforms traditional scheduling algorithms such as HEFT in terms of makespan and energy consumption.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Abhishek Kumar
AU  - Santanu Ghosh
AU  - B. Balaji Naik
AU  - Pratyay Kuila
PY  - 2024
DA  - 2024/10/04
TI  - Energy Efficient Workflow Scheduling in Cloud Computing Systems using Particle Swarm Optimization
BT  - Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023)
PB  - Atlantis Press
SP  - 266
EP  - 278
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-529-4_24
DO  - 10.2991/978-94-6463-529-4_24
ID  - Kumar2024
ER  -