Design and Evaluation of an Autonomous Load Balancing System for Mobile Data Stream Processing Based On a Data Centric Publish Subscribe Approach

Several new applications of mobile computing environments, such as Intelligent Transportation Systems, Fleet Management and Logistics, and integrated Industrial Process Automation share the requirement of remote monitoring and high performance processing of huge data streams produced by large sets of mobile nodes. Two key requirements for the deployment and operation of such mobile infrastructures are the handling of large and variable numbers of wireless connections to the monitored mobile nodes regardless of their current use or locations, and to 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 of mobile data streams. 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 great sets of mobile nodes. A significant amount of evaluation experiments of the proposed infrastructure is presented, reinforcing its viability and the benefits arising from the use of an autonomic approach to handle the requirements of high variability and scalability.