Multi-Agent Reinforcement Learning for Cooperative Task Offloading in Distributed Edge Cloud Computing

Abstract

Distributed edge cloud computing is an important computation infrastructure for Internet of Things (IoT) and its task offloading problem has attracted much attention recently. Most existing work on task offloading in distributed edge cloud computing usually assumes that each self-interested user owns one edge server and chooses whether to execute its tasks locally or to offload the tasks to cloud servers. The goal of each edge server is to maximize its own interest like low delay cost, which corresponds to a non-cooperative setting. However, with the strong development of smart IoT communities such as smart hospital and smart factory, all edge and cloud servers can belong to one organization like a technology company. This corresponds to a cooperative setting where the goal of the organization is to maximize the team interest in the overall edge cloud computing system. In this paper, we consider a new problem called cooperative task offloading where all edge servers try to cooperate to make the entire edge cloud computing system achieve good performance such as low delay cost and low energy cost. However, this problem is hard to solve due to two issues: 1) each edge server status dynamically changes and task arrival is uncertain; 2) each edge server can observe only its own status, which makes it hard to optimize team interest as global information is unavailable. For solving these issues, we formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP) which can well handle the dynamic features under partial observations. Then, we apply a multi-agent reinforcement learning algorithm called value decomposition network (VDN) and propose a VDN-based task offloading algorithm (VDN-TO) to solve the problem. Specifically, the motivation is that we use a team value function to evaluate the team interest, which is then divided into individual value functions for each edge server. Then, each edge server updates its individual value function in the direction that can maximize the team interest. Finally, we choose a part of a real dataset to evaluate our algorithm and the results show the effectiveness of our algorithm in a comparison with some other existing methods.