Load Balancing Algorithms with the application of Machine Learning: A Review

Abstract

Cloud computing is the provision of computing services over the web. Cloud Computing's load-balancing algorithms are implemented in static, dynamic, and centralized environments. The article analyzes and outlines various load-balancing techniques in the cloud computing architecture and discusses the pros and cons of different load-balancing algorithms. The research also primarily fixates on exploring machine learning models implemented in LB techniques. The most popular algorithms in the articles reviewed include Statistical Regression, Random Forest Classifier Artificial Neural Networks(RF), Convolutional Neural Networks(CNNs), and Regenerating Memory Neural Networks. long-term (LSTM-RNN). LB specifications have been defined through performance metrics such as throughput, latency, travel time, fault tolerance, and power savings.