A Statistical Machine Learning Approach to Optimize Workload in Cloud Data Centre

The modern data centre (DC) is a perplexing amalgamation of various mechanical, electrical, and control frameworks. The increasing number of possible working configurations and nonlinear interdependencies make it difficult to comprehend. To improve Data Center performance and execution, the neural network scheme can benefit from the genuinely active data. Machine Learning (ML) is emerging as the most suitable method of demonstrating DC execution and improving productivity by utilizing the existing sensor data. The data centre infrastructure, the tasks to be completed, and the desired profits are framed as a mathematical programming model, which can then be overcome by using alternative methods of searching for smart task programming. This paper proposes a novel method for maximizing the load on cloud infrastructure. This article has utilized the machine learning techniques to enhance the load balancing. The new proposed algorithm is evaluated and then compared against existing algorithms for analyzing the make span time, time taken to execute tasks, overall load on the virtual machines and total cluster utilization.