A dynamic resource allocation framework based on workload prediction algorithm for cloud computing

Abstract

The conventional load balancing algorithms feature severe limitations and drawbacks in cloud environments. In order to address these challenges, researchers have proposed prediction algorithms using genetic algorithms and genetic programming. These algorithms aim to simplify task scheduling in cloud platforms characterized by a large volume of users. The proposed scheme meets the requirements for inter-nodes load balancing. Simulations to compare the performance of the proposed scheme and the AGA demonstrated the effectiveness and validity of the proposed method in cloud computing.