Cooperative unloading and resource allocation method based on multi-agent DRL under MEC architecture

Abstract

The invention relates to a cooperative unloading and resource allocation method based on a multi-agent DRL under an MEC architecture. The method comprises the following steps: 1) proposing a collaborative MEC system architecture, and considering collaboration between edge nodes, namely, when the edge nodes are overloaded, migrating a task request to other low-load edge nodes for collaborative processing; 2) adopting a partial unloading strategy, namely unloading partial calculation tasks to an edge server for execution, and distributing the rest calculation tasks to local IoT equipment for execution; 3) modeling a joint optimization problem of a task unloading decision, a computing resource allocation decision and a communication resource allocation decision into an MDP problem according to dynamic change characteristics of task arrival; and 4) further using a multi-agent reinforcement learning collaborative task unloading and resource allocation method to dynamically allocate the resources to maximize the experience quality of users in the system. According to the method, dynamic management of the system resources under the collaborative MEC system architecture is realized, and the average delay and energy consumption of the system are reduced.

Description

technical field [0001] The invention belongs to the technical field of mobile communication, and in particular relates to a multi-agent DRL-based cooperative unloading and resource allocation method under the MEC framework. Background technique [0002] The explosive growth of smart mobile devices (SMD) and the Internet of Things (IoT) is accelerating the development of computing-intensive and latency-sensitive applications, such as virtual / augmented reality, autonomous driving, face recognition, smart cities, and smart grids, etc., resulting in It puts great pressure on mobile terminals and smart IoT devices with limited computing power [1]. Such complex applications require higher computing power, memory and battery life of SMDs [2]. Fortunately, mobile edge computing (MEC) provides an effective way to solve this problem, which deploys computing resources to edge servers close to base stations (BSs), enabling smart IoT devices to offload their computing tasks. There are ...

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Problems solved by technology

However, it only focuses on the energy consumption of edge nodes, and does not consider the resource allocation problem, nor the cooperation between edge servers.

[0004] From the above analysis, it can be clearly seen that the existing research does not take into account the cooperation between edge servers and the joint optimization of resource allocation, nor does it take into account the impact of dynamic changes in the environment, and basically The above only considers the unloading problem of one service, and does not extend to the situation of continuous arrival of multiple services

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