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Currently, the cloud computing paradigm is experiencing rapid growth as thereis a shift from other distributed computing methods and traditional ITinfrastructure towards it. Consequently, optimised task scheduling techniqueshave become crucial in managing the expanding cloud computing environment. Incloud computing, numerous tasks need to be scheduled on a limited number ofdiverse virtual machines to minimise the imbalance between the local and globalsearch space; and optimise system utilisation. Task scheduling is a challengingproblem known as NP-complete, which means that there is no exact solution, andwe can only achieve near-optimal results, particularly when using large-scaletasks in the context of cloud computing. This paper proposes an optimisedstrategy, Cuckoo-based Discrete Symbiotic Organisms Search (C-DSOS) thatincorporated with Levy-Flight for optimal task scheduling in the cloudcomputing environment to minimise degree of imbalance. The strategy is based onthe Standard Symbiotic Organism Search (SOS), which is a nature-inspiredmetaheuristic optimisation algorithm designed for numerical optimisationproblems. SOS simulates the symbiotic relationships observed in ecosystems,such as mutualism, commensalism, and parasitism. To evaluate the proposedtechnique, the CloudSim toolkit simulator was used to conduct experiments. Theresults demonstrated that C-DSOS outperforms the Simulated Annealing SymbioticOrganism Search (SASOS) algorithm, which is a benchmarked algorithm commonlyused in task scheduling problems. C-DSOS exhibits a favourable convergencerate, especially when using larger search spaces, making it suitable for taskscheduling problems in the cloud. For the analysis, a t-test was employed,reveals that C-DSOS is statistically significant compared to the benchmarkedSASOS algorithm, particularly for scenarios involving a large search space.

An optimised cuckoo-based discrete symbiotic organisms search strategy for tasks scheduling in cloud computing environment