Enhanced multi-verse optimizer for task scheduling in cloud computing environments

Cloud computing is a trending technology that allows users to use computing resources remotely in a pay-per-use model. One of the main challenges in cloud computing environments is task scheduling, in which tasks should be scheduled efficiently to minimize execution time and cost while maximizing resources' utilization. Many meta-heuristic algorithms are used for task scheduling in cloud environments in the literature such as Multi-Verse Optimizer (MVO) and Particle Swarm Optimization (PSO). In this paper, an Enhanced version of the Multi-Verse Optimizer (EMVO) is proposed as a superior task scheduler in this area. The proposed EMVO is compared with both original MVO and the PSO algorithms in cloud environments. The results show that EMVO substantially outperforms both MVO and PSO algorithms in terms of achieving minimized makespan time and increasing resources' utilization.

Automated summary

Cloud computing is a popular technology that enables users to remotely access computing resources in a pay-as-you-go model. Task scheduling is a primary challenge in cloud computing environments, with many meta-heuristic algorithms like MVO and PSO being used. The Enhanced Multi-Verse Optimizer (EMVO) proposed in this paper outperforms both MVO and PSO algorithms in terms of minimizing makespan time and increasing resource utilization in cloud environments.