Improvement of the ACO Algorithm for Intelligent Task Scheduling in Cloud Systems

Abstract

Cloud computing involves accessing and using computing resources, such as servers, storage, and software applications, over the Internet, enabling scalable access on demand. Cloud computing systems are becoming an essential platform for scientific applications. They enable task scheduling and IT resource allocation. When these resources are not enough to meet demand, planning techniques are necessary. We propose to apply an improved ACO algorithm for intelligent task scheduling and appropriate resource allocation in a cloud environment. This work proposes a modified version of the ACO algorithm that can quickly converge to the best solution to further optimize the total response time, the average response time and the total execution cost. The algorithm suggested based on artificial intelligence application of enhanced ACO is compared with the classical ACO algorithm using Cloudsim simulator. The results obtained after the experiments and simulation are very encouraging to adopt this technique.