Comparing Load-Balancer Algorithms Performance on Virtualized Environment

The need for proper workload balancing arise as there are significant increase on data traffic volume during peak time, which may cause workload imbalance or server overloading. A proposed solution to this problem is to implement a load balancer system with a load balancing algorithm that has the best performance based on incoming workloads. This paper presents a performance comparison of Round Robin, Least Connection, and Weighted Least Connection load balancing algorithms. Analysis on the experiment results is done using a machine learning model comprising three machine learning algorithms, which are AdaBoost, and Random Forest. AdaBoost predicted WLC is the best algorithm on 8 out of 10 or 80% of sampled instances. Random Forest predicted WLC is the best load balancing on 10 out of 10 or 100% of sampled instances. kNN predicted WLC is the best load balancing algorithm on 8 out of 10 or 80% of sampled instances. Based on the timestamp, amounts of threads received, and session receive rate sampled in the test dataset, the analysis found that WLC had the best performance compared to RRand LC.