HADOOP, MULTI-AGENT SYSTEMS AND MACHINE LEARNING: EXPLORING SCALABILITY, FAULT TOLERANCE AND WORKLOAD DISTRIBUTION BEHAVIORS

Abstract

Paper reports experimental results comparing several machine learning techniques performance when distributing massively parallel computation to a set of interconnected machines. Computational resources are intentionally heterogenous to simulate real ad-hoc network environment and provide realistic setting test results. Namely Round Robin, Q-Learning and Least Loaded algorithms-based solutions are examined for their scalability, fault tolerance and workload distribution behaviors. The novelty of the paper is a set of empirical set of coefficients and bottlenecks for each implementation that is free of infrastructure specifics or error and exemption handling tools for future considerations by engineering professionals and scholars.