Efficient Multi-Broker Load Balancing in Event Driven Pub-Sub Networks

Publish-Subscribe (Pub-Sub) network is a communication paradigm where publishers produce data on specific topics and subscribers subscribe to these topics. Brokers are middleware facilitating data delivery and communication based on topics. However, brokers are geographically distributed in a multi-broker environment, offering different service times, and tend to become overloaded due to the popularity of the topics when an event occurs. This results in an intolerable average delivery delay. Therefore, we propose an efficient topic-aware low latency load balancing approach that reduces the delay by handling unequal traffic distribution due to data popularity, varied request rates, and hierarchical topic ordering. The proposed approach is manifold. First, it associates the topics to a broker using a Trie data structure, identifies Hot topics based on the outlier detection method, and balances the broker load based on sojourn time modeling and heuristic approach. We use a Pub-Sub MQTT testbed to experiment with a Mosquitto broker distributed network. It reduces the clients’ average waiting time by 11%. Our approach distributes the load 20% more evenly than other approaches, and the average server utilization rate is close to 22%, which is proximal to the optimal approach and better than other approaches under study.