Autonomous Load Balancing of Data Stream Processing and Mobile Communications in Scalable Data Distribution Systems

A huge number of applications such as network monitoring, traffic engineering systems, intelligent routing of cars, sensor networks, mobile telecommunications, logistics applications and air traffic control require continuous and timely processing of high volume of data originated from many distributed sources as well as mobile communication and monitoring. The deployment and operation of infrastructures enabling such mobile communication and data stream processing have two key requirements: they must be capable of handling large and variable numbers of wireless connections to the monitored mobile nodes regardless of their current use or locations, and must automatically adapt to variations in the volume of the mobile data streams. This article describes the design, implementation, and evaluation of an autonomic mechanism for load balancing data streams and mobile connections. The autonomic capability has been incorporated into a scalable middleware system based on a Data Centric Publish Subscribe approach using the OMG Data Distribution Service (DDS) standard and aimed at real-time and adaptive handling of mobile connectivity and data stream processing for large sets of mobile nodes. Several performance evaluation experiments of the proposed infrastructure are presented, demonstrating its viability and the advantages arising from the use of an autonomic approach to handle the requirements of high variability and scalability. Keywords-Load balancing, Data Stream Processing, Autonomic computing, DDS, Mobile Communication Middleware.