Workload Diffusion Modeling for Distributed Applications in Fog/Edge Computing Environments

This paper addresses the problem of workload generation for distributed applications in fog/edge computing. Unlike most existing work that tends to generate workload data for individual network nodes using historical data from the targeted node, this work aims to extrapolate supplementary workloads for entire application / infrastructure graphs through diffusion of measurements from limited subsets of nodes. A framework for workload generation is proposed, which defines five diffusion algorithms that use different techniques for data extrapolation and generation. Each algorithm takes into account different constraints and assumptions when executing its diffusion task, and individual algorithms are applicable for modeling different types of applications and infrastructure networks. Experiments are performed to demonstrate the approach and evaluate the performance of the algorithms under realistic workload settings, and results are validated using statistical techniques.