Scalable load balancing using enhanced server health monitoring and adimission control

Abstract

The increasing number of users and services on Internet day by day resulting in high traffic and load on the web servers. This is in turn degrading the quality of service. A well known solution to this problem is to use cluster of web servers. An optimum server load balancing policy is needed to achieve scalability and high performance of the service offered by cluster of web servers. In this paper, we proposed scalable Load Balancing using enhanced server Health monitoring and Admission control (LBHA). LBHA algorithm is capable of offering guarantees for different client priorities, such as premium customers and default customers. We show that using LBHA strategy web servers are able to maintain Service Level Agreements (SLA) without the need of a priori over-dimensioning of server resources. This is achieved by taking the real perspective of the service requests using the measurement of arrival rates at that time and selectively drop some requests from the default clients if the default customers traffic is high. We analyzed and compared the experimental results of LBHA strategy with the results of the very popular load balancing algorithm, Weighted Round Robin (WRR) and also Extended Server Load Balancing Algorithm (ELBL). We show that even though the LBHA strategy takes a little more server processing resources than WRR, it is capable to render assurances unlike WRR and ELBL.