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Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

Advancing cloud load balancing : An energy-aware model using a hybrid genetic and nature-inspired algorithm

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pp. 1145–1162Vol. 47Issue 3March 2026DOI: 10.47974/JIOS-2113XML
Received:
01 Mar 2025
Published Online:
12 Mar 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2113
Pages:
1145–1162

Abstract

Cloud computing is necessary for the current global computing demand since it is becoming difficult to maintain disk space and power requirements for individual users. The dependency on computing is increasing day by day, thus increasing the pressure on the infrastructure required to meet those needs. Cloud computing is the solution to cater to this problem. When the load on a cloud is increased, it becomes imperative to manage the load or distribute the load judiciously to make job scheduling on the cloud as fair as possible. No server should ideally either be underloaded or overloaded. This equilibrium is to be maintained for the smooth functioning of the cloud environment. This research work aims to develop an energy efficient algorithm meant for the cloud setup that distributes the load evenly and has a substantial enhancing impact on the throughput of the entire system as well. These two subgoals when achieved, will permit the load distribution to move towards the main goal of achieving higher energy efficiency also and translating the entire system towards a greener system. To realize this, this paper proposes a hybrid optimization algorithm with a unification of biogeography-based optimization algorithm and genetic algorithm. The results of the new algorithm are presented in the paper and were found to lead to an increase in throughput and a reduction in scheduling cost while the number of tasks submitted is increased.

Keywords

Subject Classifications

Primary 68Q07Secondary 49N3090C90

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