Genetic and static algorithm for task scheduling in cloud computing

Abstract

Technological advancement has required ever more computing resources. In this context the cloud computing emerges as a new paradigm to meet this demand, though its resources are physically limited due to the growing data traffic that the system may be subject. The task scheduling aims to distribute tasks in order to make them more efficient in the use of computing resources. Thus, this paper aims to propose a solution to the task scheduling problem in cloud computing to reduce the processing time of the tasks and the number of virtual machines (VM). The metaheuristic genetic algorithm (GA) was used in the first stage of the algorithm, in order to reduce the processing time of the tasks. The static algorithm is designed to solve the set partitioning problem. Their performance was compared with two algorithms, classic and heuristic, along with realistic workloads.