Modeling and implementing distributed computing with applications in Multisensor-Multitarget Tracking

Abstract

Big data is a term used to describe quantities of data that are too large to process using traditional means of data processing. The rise of such quantities of data amongst virtually every industry has lead to changes in data processing paradigms that favour distributed architectures over centralized processing. However great care must be taken when designing such processing architectures as slight overhead in computing or communication can nullify gains achieved from distributing computations.

In this paper we discuss the theoretical elements involved in designing distributed systems and develop a heuristic for the performance of such a system. Our heuristic helps define when a problem requires distribution and informs designers in choosing the right topology to meet the needs of the problem and hardware involved.

Finally we present results from our own distributed computing architecture applied to a prediction problem in radar image processing.