A Review on Performance Analysis of Deep Learning for Task Offloading in Edge Computing

Abstract

Edge computing is a type of distributed computing that was designed especially for Internet of Things (IoT) users to provide computational resources and data management nearby to users' devices. Introducing edge computing for IoT networks has reduced the bandwidth and latency issue while handling real-time applications. The major benefit of edge computing is that it reduces the communication overhead between IoT user and server. Integrating IoT in our daily life has attracted researchers towards its performance management such as complexity minimization, latency minimization, memory management, energy consumption minimization, etc. The paper focuses on deep reinforcement learning to minimize the computational complexity at IoT user end. The task offloading decision process is designed using Q-Learning that minimizes the system cost and curse of high dimensional data.