Performance Evaluation of an Adaptive Middleware Load Balancing and Monitoring Service

As middleware-based distributed applications become more pervasive, the need to improve the scalability of these applications becomes increasingly important. One way to improve scalability is via load balancing. Earlier generations of middleware-based load balancing services were simplistic, however, since they focused on specific use-cases and environments, which made it hard to use these services for anything other than a small class of distributed applications. This lack of generality also often forced continuous redevelopment of application-specific load balancing services, which increases distributed applications deployment and optimization costs. Recent advances in the design and implementation of middleware-based load balancing services overcome these limitations through several techniques, including (1) support for adaptive load balancing strategies which allows a load balancer to be applied to a wide variety of applications, (2) load metric neutrality which further allows a load balancer to remain non-application specific, and (3) server-side transparency, which prevents application implementations from being complicated by adding load balancing support. This paper presents the following contributions to research on adaptive middleware-based load balancing techniques: (1) it describes the design of Cygnus, which is an adaptive load balancing/monitoring service we have developed based on the CORBA middleware standard, (2) it presents the results of benchmarking experiments that systematically evaluate different load balancing strategies supported by Cygnus to indicate empirically how they improve scalability, and (3) illustrates when adaptive load balancing is more suitable than non-adaptive load balancing for use in middleware-based distributed applications.