Dynamic load balancing in distributed content-based publish/subscribe

Distributed content-based publish/subscribe systems to date suffer from performance degradation and poor scalability caused by uneven load distributions 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 paper proposes a load balancing solution specific to 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 1.5% overhead, effective with at least 91% load estimation accuracy, and capable of distributing all of the system's load originating from an edge point of the network.