Research Article
Proposing a Meta-heuristic Algorithm Focused on Energy Consumption Improvement in Cloud Resource Scheduling
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 51 |
| Published: October 2025 |
| Authors: Rahmat Zolfaghari, Hamed Badershah |
10.5120/ijca2025925819
|
Rahmat Zolfaghari, Hamed Badershah . Proposing a Meta-heuristic Algorithm Focused on Energy Consumption Improvement in Cloud Resource Scheduling. International Journal of Computer Applications. 187, 51 (October 2025), 42-46. DOI=10.5120/ijca2025925819
@article{ 10.5120/ijca2025925819,
author = { Rahmat Zolfaghari,Hamed Badershah },
title = { Proposing a Meta-heuristic Algorithm Focused on Energy Consumption Improvement in Cloud Resource Scheduling },
journal = { International Journal of Computer Applications },
year = { 2025 },
volume = { 187 },
number = { 51 },
pages = { 42-46 },
doi = { 10.5120/ijca2025925819 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2025
%A Rahmat Zolfaghari
%A Hamed Badershah
%T Proposing a Meta-heuristic Algorithm Focused on Energy Consumption Improvement in Cloud Resource Scheduling%T
%J International Journal of Computer Applications
%V 187
%N 51
%P 42-46
%R 10.5120/ijca2025925819
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Cloud computing, as one of the most advanced computational technologies, provides extensive capabilities for resource sharing and scalability, but high energy consumption in cloud data centers has become one of the primary challenges. The goal of this research is to present a new meta-heuristic algorithm to optimize energy consumption and enhance resource efficiency in cloud environments. The proposed algorithm was implemented using simulations in real cloud environments, such as Amazon EC2 and Planet Lab. In this process, the proposed algorithm was compared with traditional algorithms like PSO, and three main metrics, including energy consumption, execution time, and resource efficiency, were evaluated. Simulation results showed that the proposed algorithm was able to reduce energy consumption by 15.8%, decrease task execution time by 14.6%, and increase resource efficiency by 10.8%.
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