计算机科学
强化学习
虚拟网络
分布式计算
嵌入
虚拟化
人工智能
理论计算机科学
云计算
操作系统
作者
Da Xiao,Andrew Zhang,Xin Liu,Yiwen Qu,Wei Ni,Ren Ping Liu
出处
期刊:IEEE Transactions on Network and Service Management
[Institute of Electrical and Electronics Engineers]
日期:2023-12-01
卷期号:20 (4): 4297-4312
被引量:1
标识
DOI:10.1109/tnsm.2023.3284293
摘要
Network Function Virtualization (NFV), which decouples network functions from hardware and transforms them into Virtual Network Functions (VNFs), is a crucial technology for data center (DC) networks. A service function chain (SFC) is composed of an ordered set of VNFs and virtual links (VLs) connecting them. To optimize the resource allocation in DC networks, we need to efficiently map SFCs onto the physical network. Nevertheless, the dynamics and diversity of SFC requests in multi-datacenter (MDC) networks pose a significant challenge in embedding SFCs. To overcome this challenge, we design a two-stage graph convolutional network (GCN) assisted deep reinforcement learning (DRL) scheme. This framework aims to maximize the overall acceptance ratio of SFC requests while minimizing the total cost in an MDC network. In the first stage, we propose a GCN-based DRL algorithm as a coarse granularity solution to the SFC embedding problem from the macro perspective. This solution outlines a local observation scope (LOS) for each agent in the multi-agent system of the second stage, where all agents simultaneously handle SFC requests from their respective DCs using a multi-agent framework from the micro perspective. Numerical evaluations show that, compared to state-of-the-art methods, the proposed scheme improves the acceptance ratio by approximately 13% compared with the Kolin algorithm and 18% compared with the DQN algorithm and saves the cost by around 28% compared with the Kolin and the DQN.
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