Dynamic Load Balancing in Distributed Content-based Publish/Subscribe

Alex Cheung.

University of Toronto, 2006.
M.A.Sc. Thesis.

Abstract

Distributed content-based publish/subscribe systems to date suffer from performance degradation and poor scalability under load conditions typical in real-world applications. The reason for this shortcoming is due to the lack of a load balancing solution, which have rarely been studied in the context of publish/subscribe. This thesis proposes a load balancing solution specific for distributed content-based publish/subscribe systems that is distributed, dynamic, adaptive, transparent, and accommodates heterogeneity. The solution consists of three key contributions: a load balancing framework, a novel load estimation algorithm, and three offload strategies. Experimental results show that the proposed load balancing solution is efficient with less than 0.7% overhead, effective with at least 90% load estimation accuracy, and capable of load balancing with 100% of load initiated at an edge node of the entire system using real-world data sets.

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