文献类型：期刊文献

英文题名：Cold-Start-Aware Cloud-Native Parallel Service Function Chain Caching in Edge-Cloud Network

作者：Zhang, Jiayin[1];Yu, Huiqun[1];Fan, Guisheng[1];Tang, Qifeng[1,2];Li, Zengpeng[1];Xu, Jin[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Data Exchange Corp, Res Inst, Shanghai 201203, Peoples R China

年份：2024

卷号：11

期号：11

起止页码：20340

外文期刊名：IEEE INTERNET OF THINGS JOURNAL

收录：;EI(收录号：20241115714228);WOS:【SCI-EXPANDED(收录号:WOS:001285460000045)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62372174; in part by the Natural Science Foundation of Shanghai under Grant 21ZR1416300; in part by the Capacity Building Project of Local Universities Science and Technology Commission of Shanghai Municipality under Grant 22010504100; in part by the Research Project Funding of Shanghai Data Exchange Corporation;and in part by the Shanghai Engineering Research Center of Smart Energy.

语种：英文

外文关键词：Cloud computing; Optimization; Internet of Things; Decision making; Substrates; Feature extraction; Topology; Cloud-native network function; cold start; edge-cloud network; Internet of Things (IoT); reinforcement learning; service function chain (SFC)

摘要：Virtualized network function (VNF) and service function chain (SFC) are the fundamental components in network functions virtualization (NFV) infrastructure, which supports the evolution of modern 5G networks. For online Internet of Things (IoT) applications, characterized by dynamic and diverse requirements, achieving optimal quality of service hinges on a resource-efficient yet performant SFC caching strategy, which is a critical challenge. Besides, despite the performance boost and flexibility brought by modern cloud-native technology, it brings the cold-start problem due to the requirement for runtime image transmission and booting-up, resulting in a nonnegligible launch latency. To tackle these challenges, this article proposes cloud-native parallel SFC caching (CPSC) framework, a novel approach to address the CPSC problem in edge-cloud networks leveraging deep reinforcement learning (DRL), seeking an efficient resource utilization of the edge-cloud network with consideration of SFC processing performance and cold-start suppressing. Graph convolutional network (GCN)-based embeddings are adopted for topology-aware feature extraction of the substrate edge-cloud network as well as the incoming SFC caching requests. Then, a pointer network (PN) is utilized for contextual information-aware caching decision making. Benefiting from the online capability of DRL, CPSC makes caching decisions in an online manner with no prior knowledge requirement on future incoming requests. Extensive simulations show that CPSC manages to outperform the state-of-the-art approaches in edge network acceptance ratio and launch latency, with minimal overhead on the SFC processing performance and decision-making duration.