Task Scheduling Optimization in Cloud Computing Based on Genetic Algorithms

Abstract

Task scheduling is the main problem in cloud computing that reduces system performance; it is an important way to arrange user needs and perform multiple goals. Cloud computing is the most popular technology nowadays and has many research potential in various areas like resource allocation, task scheduling, security, privacy, etc. To improve system performance, an efficient task-scheduling algorithm is required. Existing task-scheduling algorithms focus on task-resource requirements, CPU memory, execution time, and execution cost. In this paper, a task scheduling algorithm based on a Genetic Algorithm (GA) has been presented for assigning and executing different tasks. The proposed algorithm aims to minimize both the completion time and execution cost of tasks and maximize resource utilization. We evaluate our algorithm’s performance by applying it to two examples with a different number of tasks and processors. The first example contains ten tasks and four processors; the computation costs are generated randomly. The last example has eight processors, and the number of tasks ranges from twenty to seventy; the computation cost of each task on different processors is generated randomly. The achieved results show that the proposed approach significantly succeeded in finding the optimal solutions for the three objectives; completion time, execution cost, and resource utilization.