  Currently, the cloud computing paradigm is experiencing rapid growth as there
is a shift from other distributed computing methods and traditional IT
infrastructure towards it. Consequently, optimised task scheduling techniques
have become crucial in managing the expanding cloud computing environment. In
cloud computing, numerous tasks need to be scheduled on a limited number of
diverse virtual machines to minimise the imbalance between the local and global
search space; and optimise system utilisation. Task scheduling is a challenging
problem known as NP-complete, which means that there is no exact solution, and
we can only achieve near-optimal results, particularly when using large-scale
tasks in the context of cloud computing. This paper proposes an optimised
strategy, Cuckoo-based Discrete Symbiotic Organisms Search (C-DSOS) that
incorporated with Levy-Flight for optimal task scheduling in the cloud
computing environment to minimise degree of imbalance. The strategy is based on
the Standard Symbiotic Organism Search (SOS), which is a nature-inspired
metaheuristic optimisation algorithm designed for numerical optimisation
problems. SOS simulates the symbiotic relationships observed in ecosystems,
such as mutualism, commensalism, and parasitism. To evaluate the proposed
technique, the CloudSim toolkit simulator was used to conduct experiments. The
results demonstrated that C-DSOS outperforms the Simulated Annealing Symbiotic
Organism Search (SASOS) algorithm, which is a benchmarked algorithm commonly
used in task scheduling problems. C-DSOS exhibits a favourable convergence
rate, especially when using larger search spaces, making it suitable for task
scheduling problems in the cloud. For the analysis, a t-test was employed,
reveals that C-DSOS is statistically significant compared to the benchmarked
SASOS algorithm, particularly for scenarios involving a large search space.
