Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing

This paper proposes the combination of Swann Intelligence algorithm ofartific a! her colony caalh heuristic scheduling algorithm, called Heuristic `task Scheduling with Artificial lice Colony (UMW). This algorithm is applied to improve virtual machines scheduling solution for cloud computing within homogeneous and heterogeneous environments. It was introduced to minimize makespan and balance the loads. The scheduling performance of the cloud computing system With HABC was compared to that supplemented with other swarm intelligence algorithms: Ant Colony Optimization (ACO) with standard heuristic algorithm, Particle Swarm Optimization (PSO) with standard heuristic algorithm and improved PSO (IPSO) with standard heuristic algorithm. In our experiments, CloudSim was used to simulate systems that used different supplementing algorithms for the purpose of comparing their makespan and load balancing capability. The experimental results can he concluded that virtual machine scheduling management with artificial bee colony algorithm and largest job first (HABC_LJT) outperformed those with ACO, PSO, and IPSO. (C) 2020 The Authors. Published by Atlantis Press SARI.