G/M/1‐Based DDoS Attack Mitigation in 5G Ultradense Cellular Networks
Qinghang Gao, Hao Wang, Liyong Wan, Jianmao Xiao, Long Wang · Wireless Communications and Mobile Computing · 2022
With the 5G millimeter wave (mmWave) application, ultradense cellular networks are gradually becoming one of the core characteristics of 5G cellular networks. In the edge computing environment, considering load balancing among edge nodes is beneficial to slow down the process of distributed denial of service (DDoS) attack. However, most existing studies have given less consideration to congestion in the multiuser and multiedge server models. Someone who uses the M/M/1 model also seems to ignore the effect of scheduling algorithms on the Markov property of the task arrival process. In this manuscript, based on ensuring the quality of experience (QoE) for users, the G/M/1 model is introduced to the task scheduling of edge servers for the first time to improve load balancing between edge servers. For the multi armed bandit (MAB) algorithm framework, specific metrics are established to quantify the degree of its equilibrium. The number of users assigned to the edge nodes and each edge node’s processing of specific tasks is taken into account. We experimentally evaluated its performance against two baseline approaches and three state‐of‐the‐art approaches on a real‐world dataset. And the experimental results validate the effectiveness of this method.