Dynamic load balancing in distributed contentbased publish/subscribe

Distributed content-based publish/subscribe systems to date suffer from performance degrada-tion 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 con-tributions: 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. ii Acknowledgements Super thanks to Mom, Dad, sister (Annie), friends (Mr. Biggy, Mr