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Cloud computing is currently a very popular computing paradigm as it provides ubiquitous, on-demand access as a service to computing resources via the Internet. In spite of offering marked advantages over the traditional style of computing, there are several issues related to load on the computing system and task scheduling to outperform the computation that need to be effectively solved in order to provide better quality of service to the service consumer. Task scheduling is a crucial research area since it affects the system load and performance; and there will always be scope for optimizing existing scheduling algorithms and propose efficient new task scheduling algorithms. Many task scheduling algorithms to resolve this problem have already been proposed — Particle Swarm Optimization, Ant Colony Optimization, Genetic algorithms, Artificial Bee Algorithm etc. In this paper, we propose a hybrid task scheduling algorithm that is based on combining the plus points of bio-inspired algorithms like Ant Colony Optimization and Artificial Bee Algorithm. We show were the strong points of both these algorithms can be utilized and incorporated in order to optimize task scheduling in the cloud algorithm. It is observed that the proposed algorithm gave an improvement of about 19% when compared to the default FCFS scheduling strategy, 11% better than ABC algorithm and performed 9% better than the conventional ACO based task scheduling.
Madivi et al. (Tue,) studied this question.