Towards Deploying Parallelized Service Function Chains Under Dynamic Resource Request in Multi-Access Edge Computing

作     者：Zhang, Dongliang Wang, Lei Rezaeipanah, Amin 

作者机构：Henan Open University School of Information Engineering and Artificial Intelligence Zhengzhou450046 China Henan Polytechnic University School of Computer Science and Technology Jiaozuo454003 China Persian Gulf University Department of Computer Engineering Bushehr Iran 

出 版 物：《IEEE Transactions on Network and Service Management》 (IEEE Trans. Netw. Serv. Manage.)

年 卷 期：2024年

核心收录：

学科分类：1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Network function virtualization 

摘      要：Resource distribution policy and how to assemble the Service Function Chain (SFC) in Multi-access Edge Computing (MEC) networks to meet service quality standards poses an important challenge for Network Function Virtualization (NFV) technology. Increasing the number of Virtual Network Functions (VNFs) leads to high-latency SFC assembly, which can be countered by network function parallelization. However, existing studies parallelize VNF for resource allocation in MEC by assuming that the demanded resources do not change during SFC assembly. To address these issues, this paper develops a Latencyaware VNF Parallelization strategy under Resource demand Uncertainty (LVPRU) in MEC. We formulate LVPRU under the assumption of resource uncertainty in MEC via Quadratic Integer Programming (QIP) and show that the problem is *** parallelizes VNFs by discovering dependencies between them and assembles multiple sub-SFCs instead of the original SFC. We apply Asynchronous Advantage Actor-Critic (A3C) as a deep reinforcement learning algorithm to assemble sub-SFCs. We finally evaluate the performance of LVPRU through trace-driven simulations. The evaluation results of proposed strategy are promising in different scenarios compared to benchmark algorithms. © 2004-2012 IEEE.