Secure Mobile Edge Server Placement Using Multi-Agent Reinforcement Learning

Abstract

Mobile edge computing is capable of providing high data processing capabilities while ensuring low latency constraints of low power wireless networks, such as the industrial internet of things. However, optimally placing edge servers (providing storage and computation services to user equipment) is still a challenge. To optimally place mobile edge servers in a wireless network, such that network latency is minimized and load balancing is performed on edge servers, we propose a multi-agent reinforcement learning (RL) solution to solve a formulated mobile edge server placement problem. The RL agents are designed to learn the dynamics of the environment and adapt a joint action policy resulting in the minimization of network latency and balancing the load on edge servers. To ensure that the action policy adapted by RL agents maximized the overall network performance indicators, we propose the sharing of information, such as the latency experienced from each server and the load of each server to other RL agents in the network. Experiment results are obtained to analyze the effectiveness of the proposed solution. Although the sharing of information makes the proposed solution obtain a network-wide maximation of overall network performance at the same time it makes it susceptible to different kinds of security attacks. To further investigate the security issues arising from the proposed solution, we provide a detailed analysis of the types of security attacks possible and their countermeasures.

Energy-aware edge server placement using the improved butterfly optimization algorithm

“…Then tasks are distributed on the servers based on their processing capacity. Some other techniques used in this field are integer programming based [20], learning based [21], game theory based [22], Dynamic Programming based [23], and Approximation Algorithm based [24].…”

Multi-Objective Edge Server Placement Using the Whale Optimization Algorithm and Game Theory

“…Another category of server placement methods is based on machine learning models. In [20], a method of server placement using multi-agent reinforcement learning is introduced with the objectives of reducing resource access delay and balancing workload on cloud servers. Each of the multi-objective learning agents tries to reduce the resource access delay and better workload distribution on edge servers.…”

“…Three similar and new algorithms have been used to evaluate the proposed method. The first algorithm uses the multiagent reinforcement learning (MARL) [20] technic. In this method, agents try to learn the dynamics of the environment and optimal resource placement with the objectives of reducing latency and better load balance of resources.…”

Edge Computing for Internet of Things

“…The approach focuses on heterogeneous devices and the dynamic nature of the end devices. The appropriate placing of these edge resources is the focus of [6], which attempts to use reinforcement learning to optimize placement based on the latency and load of the server. The authors analyze the effectiveness of the proposed solution to attempt to maximize network-wide performance and focus on improving security using this approach.…”