Load Balancing for Multiedge Collaboration in Wireless Metropolitan Area Networks: A Two-Stage Decision-Making Approach

Abstract

Mobile edge computing (MEC) relieves the latency and energy consumption of mobile applications by offloading computation-intensive tasks to nearby edges. In wireless metropolitan area networks (WMANs), edges can better provide computing services via advanced communication technologies. For improving the Quality-of-Service (QoS), edges need to be collaborated rather than working alone. However, the existing solutions of multi-edge collaboration solely adopt a centralized or decentralized decision-making way of load balancing, making it hard to achieve the optimal result because the local and global conditions are not jointly considered. To solve this problem, we propose a novel Two-stage Decision-making method of load Balancing for multi-Edge Collaboration (TDB-EC). First, the centralized decision-making is executed with global information, where a deep neural networks (DNN) based prediction model is designed to evaluate the range of task scheduling between adjacent edges. Next, considering the global condition of load balancing, the decentralized decision-making is executed with local information, where a deep Q-networks (DQN) based Q-value prediction model of adjustment operations is developed to evaluate the load balancing plan among edges. Finally, the objective load balancing plan is obtained via feedback-control. Extensive simulation experiments demonstrate the adaptability of the TDB-EC to various scenarios of multi-edge load balancing, which approximates the optimal result and outperforms three classic methods.