A Task Scheduling Algorithm Based on Genetic Algorithm and Ant Colony Optimization Algorithm with Multi-QoS Constraints in Cloud Computing

Abstract

Task scheduling problem in cloud computing environment is NP-hard problem, which is difficult to obtain exact optimal solution and is suitable for using intelligent optimization algorithms to approximate the optimal solution. Meanwhile, quality of service (QoS) is an important indicator to measure the performance of task scheduling. In this paper, a novel task scheduling algorithm MQoS-GAAC with multi-QoS constraints is proposed, considering the time-consuming, expenditure, security and reliability in the scheduling process. The algorithm integrates ant colony optimization algorithm (ACO) with genetic algorithm (GA). To generate the initial pheromone efficiently for ACO, GA is invoked. With the designed fitness function, 4-dimensional QoS objectives are evaluated. Then, ACO is utilized to seek out the optimum resource. The experiment indicates that the proposed algorithm has preferable performance both in balancing resources and guaranteeing QoS.