A predictive and probabilistic load-balancing algorithm for cluster-based web servers

Abstract

The exponential demands for high performance web servers led to use of cluster-based web servers. This increasing trend continues as dynamic contents are changing traditional web environments. Increasing utilization of cluster web servers through effective and fair load balancing is a crucial task specifically when it comes to advent of dynamic contents and database-driven applications on the internet. The proposed load-balancing algorithm classifies requests into different classes. The algorithm dynamically selects a request from a class and assigns the request to a server. For both the scheduling and dispatching, new probabilistic algorithms are proposed. To avoid using unreliable measured utilization in the face of fluctuating loads the proposed load-balancing algorithm benefits from a queuing model to predict the utilization of each server. We also used a control loop feedback to adjust the predicted values periodically based on soft computing techniques. The implementation results, using standard benchmarks confirms the effectiveness of proposed load-balancing algorithm. The algorithm significantly improves both the throughput and mean response time in contrast to two existing load-balancing algorithms.