Journal article

A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments

M Goudarzi, MS Palaniswami, R Buyya

IEEE Transactions on Mobile Computing | Published : 2021

Abstract

Fog/Edge computing is a novel computing paradigm supporting resource-constrained Internet of Things (IoT) devices by placement of their tasks on edge and/or cloud servers. Recently, several Deep Reinforcement Learning (DRL)-based placement techniques have been proposed in fog/edge computing environments, which are only suitable for centralized setups. The training of well-performed DRL agents requires manifold training data while obtaining training data is costly. Hence, these centralized DRL-based techniques lack generalizability and quick adaptability, thus failing to efficiently tackle application placement problems. Moreover, many IoT applications are modeled as Directed Acyclic Graphs (..

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