Hybrid Spotted Hyena and Simulated Annealing Optimization Algorithm(HSHOSAA-1) for Efficient Task Scheduling in a Clustered Cloud Environment

Efficient task scheduling in the cloud is vital as it maximises the utilisation of resources, guaranteeing optimal allocation of computing resources such as CPU and memory. It improves the efficiency of the system by reducing the amount of time it spends idle and maximising the amount of work that can be done by applications and services. This article presents a new way for optimization algorithms by combining two approaches: the Spotted Hyena Optimisation algorithm (SHO) and the Simulated Annealing algorithm (SA). SHO utilises the cooperative hunting behaviours of hyenas to investigate the issue space and discover the most efficient or nearly perfect solutions for optimising job scheduling. SA enhances SHO by repeatedly refining solutions, resulting in increased convergence rates and attaining superior task scheduling results. The problem involves the selection of appropriate virtual machines (VMs) and the allocation of executors (resources), like a Bin Packing problem, which is a computationally difficult optimization work known as NP-Hard. The objective of our proposed HSHOSA-1 is to evaluate the requirements of tasks and the resources that are available for task scheduling. The main goal is to optimize task scheduling by minimizing expenses, preventing virtual machine overload, and ensuring balanced resource utilization. By conducting simulations on the CloudSim platform, the experimental findings show that the approach effectively decreases the number of virtual machines (VMs) needed to deploy all executors. This methodology efficiently reduces the total expenditure on resources in comparison to conventional scheduling strategies.