Edge Intelligence for Energy-efficient Computation Offloading and Resource Allocation in 5G Beyond

TL;DR

This paper proposes a DRL-based strategy for energy-efficient computation offloading and resource allocation in 5G networks, effectively managing diverse application needs and limited network information.

Contribution

It introduces a novel DRL algorithm for joint offloading and resource allocation in multi-user end-edge-cloud networks, optimizing energy consumption.

Findings

DRL algorithm reduces system energy consumption significantly.

Performance depends on learning rate, discount factor, and number of devices.

Outperforms benchmark policies in real-world scenarios.

Abstract

5G beyond is an end-edge-cloud orchestrated network that can exploit heterogeneous capabilities of the end devices, edge servers, and the cloud and thus has the potential to enable computation-intensive and delay-sensitive applications via computation offloading. However, in multi user wireless networks, diverse application requirements and the possibility of various radio access modes for communication among devices make it challenging to design an optimal computation offloading scheme. In addition, having access to complete network information that includes variables such as wireless channel state, and available bandwidth and computation resources, is a major issue. Deep Reinforcement Learning (DRL) is an emerging technique to address such an issue with limited and less accurate network information. In this paper, we utilize DRL to design an optimal computation offloading and resource allocation strategy for minimizing system energy consumption. We first present a multi-user end-edge-cloud orchestrated network where all devices and base stations have computation capabilities. Then, we formulate the joint computation offloading and resource allocation problem as a Markov Decision Process (MDP) and propose a new DRL algorithm to minimize system energy consumption. Numerical results based on a real-world dataset demonstrate that the proposed DRL-based algorithm significantly outperforms the benchmark policies in terms of system energy consumption. Extensive simulations show that learning rate, discount factor, and number of devices have considerable influence on the performance of the proposed algorithm.