Cooperative Multi-Agent Deep Reinforcement Learning for Dynamic Virtual Network Allocation With Traffic Fluctuations

Abstract

Network traffic and computing demand have been changing dramatically due to the growth of various types of network services, e.g., high-quality video delivery and operating system (OS) updates. To maximize the utilization efficiency of limited network resources, network resource control technology is required for smooth and quick operation when network demands change. Therefore, we propose a dynamic virtual network (VN) allocation method based on cooperative multi-agent deep reinforcement learning (Coop-MADRL). This method can quickly optimize network resources even while network demands are drastically changing by learning the relationship between network demand patterns and optimal allocation by using deep reinforcement learning (DRL) in advance. The key idea is to use a multi-agent technique for a reinforcement learning (RL) based dynamic VN allocation method, which can reduce the number of candidate actions per agent and can improve the performance for VN allocation. Moreover, a cooperation technique improves the efficiency of VN allocation. From results of a simulation evaluation, Coop-MADRL can calculate effective allocation within 1 s, which reduces the maximum server and link utilization and drastically reduces the constraint violations compared with that of the static VN allocation method. Furthermore, we revealed that the learning with various mixed traffic models could achieve a high generalization performance for all traffic patterns.