Application Placement in Fog Environments using Multi-Objective Reinforcement Learning with Maximum Reward Formulation

Abstract

The service placement problem considers the placement of multiple connected services across a heterogeneous device network and is one of the core problems of fog computing. We discuss the complexity of this service placement problem, and propose a model for solving it using Multi-Objective Reinforcement Learning (MORL) methodologies. Using a trained neural network greatly reduces the resource consumption of the placement algorithm, making it viable for resource-constrained scenarios. Starting from state-of-the-art techniques, we develop a generic max reward formulation model and apply several MORL methodologies, which solve the placement problem in scenarios where the preference weights change. We compare the results to a baseline methodology and showcase the value of MORL on the placement problem.