Offbeat Load Balancing Machine Learning based Algorithm for Job Scheduling

In cloud computing environments, parallel processing is required for largescale computing tasks. Two different tasks are taken, and these tasks are independent of each other. These tasks are independently applied to Virtual Machines (VM). We proposed Offbeat Load Balancing (LB) Machine Learning algorithm using a task scheduling algorithm in Cloud Computing (CC) environments to reduce execution time. In this paper, the proposed algorithm is based on the concept of Random Forest Classifier and Genetic Algorithm and K-Means clustering algorithm for optimized load. The proposed algorithm shows that the average execution time of 3.5104 seconds (20 jobs, 5 Machines) and 15.85 seconds (20 jobs, 10 machines) is based on a study of load balancing algorithms that needs less execution time than other algorithms.