Optimizing Resource Fragmentation in Virtual Network Function Placement Using Deep Reinforcement Learning

作     者：Mohamed, Ramy Avgeris, Marios Leivadeas, Aris Lambadaris, Ioannis 

作者机构：Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada École de technologie supérieure Department of Software and IT Engineering MontrealQCH3C 1K3 Canada 

出 版 物：《IEEE Transactions on Machine Learning in Communications and Networking》 (IEEE. Trans. Mach. Learn. Commun. Netw.)

年 卷 期：2024年第2卷

页      面：1475-1491页

核心收录：

基　　金：Ericsson Canada Natural Sciences and Engineering Research Council of Canada (NSERC) 

主　　题：Constrained optimization 

摘      要：In the 6G wireless era, the strategical deployment of Virtual Network Functions (VNFs) within a network infrastructure that optimizes resource utilization while fulfilling performance criteria is critical for successfully implementing the Network Function Virtualization (NFV) paradigm across the Edge-to-Cloud continuum. This is especially prominent when resource fragmentation -where available resources become isolated and underutilized- becomes an issue due to the frequent reallocations of VNFs. However, traditional optimization methods often struggle to deal with the dynamic and complex nature of the VNF placement problem when fragmentation is considered. This study proposes a novel online VNF placement approach for Edge/Cloud infrastructures that utilizes Deep Reinforcement Learning (DRL) and Reward Constrained Policy Optimization (RCPO) to address this problem. We combine DRL s adaptability with RCPO s constraint incorporation capabilities to ensure that the learned policies satisfy the performance and resource constraints while minimizing resource fragmentation. Specifically, the VNF placement problem is first formulated as an offline-constrained optimization problem, and then we devise an online solver using Neural Combinatorial Optimization (NCO). Our method incorporates a metric called Resource Fragmentation Degree (RFD) to quantify fragmentation in the network. Using this metric and RCPO, our NCO agent is trained to make intelligent placement decisions that reduce fragmentation and optimize resource utilization. An error correction heuristic complements the robustness of the proposed framework. Through extensive testing in a simulated environment, the proposed approach is shown to outperform state-of-the-art VNF placement techniques when it comes to minimizing resource fragmentation under constraint satisfaction guarantees. © 2024 2024 The Authors.