Optimizing task scheduling in cloud computing: a hybrid artificial intelligence approach

Abstract

AbstractThe artificial intelligence-based allocation of resources can substantially reduce resource wastage and cost. Cloud resource allocation and management have appeared to be the central research direction. Computing through the cloud looks like improvements in network, parallel, and distributed computing. This presentation explains how scheduling works in a cloud environment by comparing it to natural selection, a concept that describes biological evolution. A strategy to reduce the time needed to find a solution by proposing an optimal solution. algorithm adds flexibility, adaptability, parallel processing, and global optimization. Furthermore, the cost function is used to find a beneficial solution by studying the operational completion time, cost of resources, and load balancing. benchmark problems tested through the algorithm show that it performs better than the other algorithms. The algorithm simultaneously solves the scheduling tasks of virtual machines and self-guided vehicles in a cloud computing environment. We Also tested for significant differences among algorithms and the number of jobs using an analysis of variance. Finally, task scheduling using the suggested algorithm indicates an improvement.

Keywords

• Optimizing artificial intelligence
• cloud computing
• natural selection
• biological evolution task scheduling
• heterogeneous integer programming