Task Scheduling with Dynamic Load Balancing for Cloud Computing

Abstract

Using powerful cryptographic techniques and complex machine learning algorithms, this study explores an enhanced way for dynamic job scheduling, data recovery, and workflow management in cloud computing. To improve data recovery procedures and optimize job scheduling, we investigate the combination of the Random Forest, Q-learning from artificial neural networks (ANNs), and the J48 decision tree algorithms. To ensure data redundancy and integrity, we also use the Kochi matrix for effective data chunk generation and backup. The Advanced Encryption Standard (AES) is used to protect data. Our all-encompassing approach is to increase the effectiveness of resource allocation, hasten data recovery, and offer strong security in cloud environments. The efficiency of the suggested methodologies in improving overall system performance, security, and reliability is highlighted by the experimental findings, which show notable advancements over conventional approaches. Key Words: Load Balancing, Machine Learning, ANN, Random Forest, AES, Q-learning