salbnet: A Self-Adapting Load Balancing Network

Jörg Jung, Bettina Schnor, and Sebastian Menski

Keywords

Cluster Computing, Load Balancing, Performance Evaluation

Abstract

Server Load Balancing (SLB) is a popular technique to build high-availability web services as offered from Google and Amazon for example. Credit based load balancing strategies have been proposed in the literature where the back end servers dynamically report a metric called Credit to the Load Balancer (LB) which reflects their current capacity. This enables the LB to adapt the load balancing strategy. The benefit of Credit based SLB has been shown by simulations, but up to now, it is not used in productive systems, since efficient implementations were missing. This paper presents the evaluation of an implementation of Credit based SLB, the so-called Self-Adapting Load Balancing Network (salbnet). We evaluate salbnet for a cluster of web servers. The measurements are done with a representative workload based on a Wikipedia trace and confirm the benefit of the self-adapting load balancing approach.

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