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Meta-Heuristic Scheduling: A Review on Swarm Intelligence and Hybrid Meta-Heuristics Algorithms for Cloud Computing

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Abstract

Numerous processing and storage resources are available through pay-per-use cloud computing. Cloud resources are managed by data centers based on demand, availability, and other factors like reliability and security. Due to task size and workflow interdependence, task scheduling is a complex process that impacts overall system performance. By considering factors like cost, failure rate, and makespan that influence task scheduling, the goal is to achieve optimal task scheduling among the resources. Meta-heuristics strategies are used extensively in research to solve task-scheduling issues. This study presents an overview of meta-heuristics in general and a comparative analysis of swarm intelligence-based meta-heuristic algorithms used in cloud task scheduling. It has been observed that scheduling performance has been enhanced by leveraging the advantages of diverse meta-heuristic algorithms in hybrid methods. The different meta-heuristic algorithms, environments, simulation tools, scheduling objectives, and metrics that go along with them are compared.

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This work is part of the research supported by the Indian Council for Cultural Relations.

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  1. Department of Computer Science, University of Kerala, 695581, Kariavattom, Trivandrum, Kerala, India

    Samah Jomah & Aji S

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Jomah, S., S, A. Meta-Heuristic Scheduling: A Review on Swarm Intelligence and Hybrid Meta-Heuristics Algorithms for Cloud Computing. Oper. Res. Forum 5, 94 (2024). https://doi.org/10.1007/s43069-024-00382-0

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