Recently, cloud computing has begun to experience tremendous growth because government agencies and private organisations are migrating to the cloud environment. Hence, having a task scheduling strategy that is efficient is paramount for effectively improving the prospects of cloud computing. Typically, a certain number of tasks are scheduled to use diverse resources (virtual machines) to minimise the makespan and achieve the optimum utilisation of the system by reducing the response time within the cloud environment. The task scheduling problem is NP-complete; as such, obtaining a precise solution is difficult, particularly for large-scale tasks. Therefore, in this paper, we propose a metaheuristic enhanced discrete symbiotic organism search (eDSOS) algorithm for optimal task scheduling in the cloud computing setting. Our proposed algorithm is an extension of the standard symbiotic organism search (SOS), a nature-inspired algorithm that has been implemented to solve various numerical optimisation problems. This algorithm imitates the symbiotic associations (mutualism, commensalism, and parasitism stages) displayed by organisms in an ecosystem. Despite the improvements made with the discrete symbiotic organism search (DSOS) algorithm, it still becomes trapped in local optima due to the large size of the values of the makespan and response time. The local search space of the DSOS is diversified by substituting the best value with any candidate in the population at the mutualism phase of the DSOS algorithm, which makes it worthy for use in task scheduling problems in the cloud. Thus, the eDSOS strategy converges faster when the search space is larger or more prominent due to diversification. The CloudSim simulator was used to conduct the experiment, and the simulation results show that the proposed eDSOS was able to produce a solution with a good quality when compared with that of the DSOS. Lastly, we analysed the proposed strategy by using a two-sample t-test, which revealed that the performance of eDSOS was of significance compared to the benchmark strategy (DSOS), particularly for large search spaces. The percentage improvements were 26.23% for the makespan and 63.34% for the response time.

No abstract

In the past few years nature-inspired algorithms are experiencing rapid growth where most optimisation problems in different domains are addressed using it. As a result of this development come the issue of handling a complex optimisation problem within a short period remains very difficult. Symbiotic organisms search (SOS) algorithm is one of the nature-inspired metaheuristics that mimics the symbiotic association of organisms in an ecosystem. This paper proposes to investigate symbiotic organisms search algorithms used in handling various optimisation problems in different fields to bring out strengths and weaknesses of the existing algorithms as well as to point out future directions for the upcoming studies in the domain. To achieve that, studies done in optimisation problems using symbiotic organisms search from 2014 – 2020 that are obtained from some databases (Scopus, ScienceDirect, IEEE Xplore, ACM) were surveyed; where the review of various issues related to SOS such as diversity of solution search space, variants, scalability, and applications of the SOS. Finally, future research directions in the area were recommended.