Task Scheduling in Cloud Computing Using Hybrid Meta-heuristic: A Review

TL;DR

This paper reviews hybrid meta-heuristic algorithms for task scheduling in cloud computing, highlighting their effectiveness in resource optimization and cost efficiency compared to single-method approaches.

Contribution

It provides a comprehensive analysis of various hybrid meta-heuristic techniques and their performance metrics in cloud task scheduling.

Findings

Hybrid algorithms improve resource utilization.

Meta-heuristics outperform traditional scheduling methods.

Performance metrics vary across techniques.

Abstract

In recent years with the advent of high bandwidth internet access availability, the cloud computing applications have boomed. With more and more applications being run over the cloud and an increase in the overall user base of the different cloud platforms, the need for highly efficient job scheduling techniques has also increased. The task of a conventional job scheduling algorithm is to determine a sequence of execution for the jobs, which uses the least resources like time, processing, memory, etc. Generally, the user requires more services and very high efficiency. An efficient scheduling technique helps in proper utilization of the resources. In this research realm, the hybrid meta-heuristic algorithms have proven to be very effective in optimizing the task scheduling by providing better cost efficiency than when singly employed. This study presents a systematic and extensive analysis of task scheduling techniques in cloud computing using the various hybrid variants of meta-heuristic methods, like Genetic Algorithm, Tabu Search, Harmony Search, Artificial Bee Colony, Particle Swarm Optimization, etc. In this research review, a separate section discusses the use of various performance evaluation metrics throughout the literature.