Genetic Algorithm of Independent Task Meta-Scheduling Centralized in the Cloud Computing

Abstract

A group of networked, virtualized computers make up the distributed, parallel cloud computing technology. The power for these machines is dynamic, and they are displayed as one or more computing resources. These are compiled based on service level agreements (SLAs) that have been negotiated between the service provider and the customers. Enterprise applications have migrated in large numbers to cloud computing during the past few years. One of the most important challenges of Cloud Computing is the scheduling of tasks; which should satisfy Cloud users in terms of Quality of Service and increase the profit of cloud providers. Bio-inspired algorithms (genetics) represent a heuristic research technique that produces effective solutions. In this article, we propose a genetic meta-scheduling algorithm that optimizes the execution time and makespan of tasks submitted by users. To achieve this, this algorithm is based on the requirements of user requests and the availability of resources (Virtual Machines) of Cloud Computing to obtain a better combination as an optimal solution. This effort makes the meta-scheduling genetic algorithm superior than others in the literature like the Min-Min algorithm and the regular genetic algorithm. Customer satisfaction is higher, and more particularly, the execution time and makespan are better.