Metaheuristic Algorithms Based Server Consolidation for Tasks Scheduling in Cloud Computing Environment

Abstract

Due to the large migration of applications from traditional computing environments to cloud systems, task scheduling has become a well-known research topic. Further, assigning tasks is an essential step in the server consolidation that enables clients’ needs to be mapped to suitable resources, such as Physical Machines (PMs) or Virtual Machines (VMs). The difficulty lies in using the available shared computing resources for various tasks without degrading the quality of service (QoS) or increasing the carbon footprint. In addition, the cloud provider needs to use a good task strategy based on the requirements of the client and the IT resources capacity. The best resource utilization and quickest task execution completion times are essential metrics for a cloud provider to maintain the optimum performance of the cloud system. The appropriate solution distributes tasks to the available virtual machine while applying optimization techniques such as the artificial bee colony (ABC), the cuckoo search (CS) algorithm, and particle swarm optimization (PSO). This paper will analyze the algorithms mentioned above based on energy consumption and resource utilization.