Dynamic Rate Control for Topic-based Pub/Sub Systems

Pub/sub systems have been widely utilized in the industry as the connecting piece between high rate producers or mobile clients and end-services, due to their ability to handle messages of high volume and velocity and achieve high throughput. Apache Kafka is one of the most popular Big Data messaging systems. Although Kafka's modular Consumer API allows businesses to take advantage of the rich set of features it provides, it often suffers from improper configured topics/partitions or load imbalances due to spikes or high volume of messages injected in the same topic. In our research our goal is to develop a novel framework which investigates smart queuing mechanisms to overcome these issues and deal effectively with sudden bursts and overloads which are frequently experienced in these systems. Our approach provides rate control capabilities to the Kafka's consumer API which allows us to effectively meet the requirements of different end-services without interference among them.