Levy Flight Osprey Optimization Algorithm for Task Scheduling in Cloud Computing

Task scheduling in a Cloud Computing (CC) environment offers flexible and scalable computing resources that encounter critical challenges that significantly affect the system performance. The existing methods often suffer from minimal convergence rates due to ineffective task scheduling, leading to a degradation in the quality of task scheduling in the CC environment. This paper proposes the Levy Flight Osprey Optimization Algorithm (LFOOA) for task scheduling, which provides high-quality scheduling solutions due to its effective search mechanisms. The novel approach aims to improve the convergence rate and reduce the time required for evaluation. The significance of task scheduling in CC lies in its responsibility for assigning tasks to each virtual machine, optimising execution time, and minimizing energy consumption. The existing methods namely, Particle Swarm Optimization (PSO), Random Double Adaptive Whale Optimization Algorithm (RDWOA) and Whale Optimization Algorithm (WOA) and PS-Grey Wolf Optimization (PSGWO) are evaluated in the proposed method. The LDOOA approach achieves better performance with a makespan of 97, resource utilization of 99%, and energy consumption of 1.1.