{"title":"An Efficient Algorithm for Service Function Chains Reconfiguration in Mobile Edge Cloud Networks","authors":"Biyi Li, B. Cheng, Junliang Chen","doi":"10.1109/ICWS53863.2021.00062","DOIUrl":null,"url":null,"abstract":"Mobile Edge Computing (MEC) supports ultra-low latency and high-bandwidth services as an emerging network architecture by deploying servers at the edge of the network to provide computing and storage resources. Along with the MEC technology, Network Function Virtualization (NFV) provisions Service Function Chains (SFC) on MEC servers to improve user service experience and achieve fast access to the mobile user. However, users are constantly moving in the edge network, and different users usually have different delay requirements for service requests. To guarantee the QoS of mobile users, it is necessary to migrate SFCs to an advisable edge server when users move across Base Stations (BS). This paper focuses on the SFCs reconfiguration scheme with resource capacity constraints in the MEC network to support the seamless migration of mobile user services. We first formalize the SFCs reconfiguration problem of the edge network as a mathematical model, which aims to minimize the end-to-end delay and operating costs of user services. Then, we convert the problem into an equivalent shortest path problem and design a Dynamic Programmingbased SFC Migration algorithm (DPSM). Finally, we conduct simulation experiments to evaluate the performance of the algorithm based on a real-world dataset. The experiment results show the effectiveness and efficiency of our algorithm.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Web Services (ICWS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICWS53863.2021.00062","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Mobile Edge Computing (MEC) supports ultra-low latency and high-bandwidth services as an emerging network architecture by deploying servers at the edge of the network to provide computing and storage resources. Along with the MEC technology, Network Function Virtualization (NFV) provisions Service Function Chains (SFC) on MEC servers to improve user service experience and achieve fast access to the mobile user. However, users are constantly moving in the edge network, and different users usually have different delay requirements for service requests. To guarantee the QoS of mobile users, it is necessary to migrate SFCs to an advisable edge server when users move across Base Stations (BS). This paper focuses on the SFCs reconfiguration scheme with resource capacity constraints in the MEC network to support the seamless migration of mobile user services. We first formalize the SFCs reconfiguration problem of the edge network as a mathematical model, which aims to minimize the end-to-end delay and operating costs of user services. Then, we convert the problem into an equivalent shortest path problem and design a Dynamic Programmingbased SFC Migration algorithm (DPSM). Finally, we conduct simulation experiments to evaluate the performance of the algorithm based on a real-world dataset. The experiment results show the effectiveness and efficiency of our algorithm.
移动边缘计算(MEC)是一种新兴的网络架构,通过在网络边缘部署服务器,提供计算和存储资源,支持超低延迟和高带宽业务。与MEC技术相结合,NFV (Network Function Virtualization)在MEC服务器上提供SFC (Service Function Chains)功能,提升用户服务体验,实现移动用户的快速接入。但是,在边缘网络中,用户是不断移动的,不同的用户通常对业务请求有不同的延迟要求。为了保证移动用户的QoS,当用户跨基站移动时,需要将sfc迁移到合适的边缘服务器上。研究了MEC网络中具有资源容量约束的sfc重构方案,以支持移动用户业务的无缝迁移。我们首先将边缘网络的sfc重构问题形式化为一个数学模型,该模型旨在最大限度地降低用户服务的端到端延迟和运营成本。然后,将该问题转化为等效最短路径问题,设计了基于动态规划的SFC迁移算法(DPSM)。最后,我们进行了基于真实数据集的仿真实验来评估算法的性能。实验结果表明了该算法的有效性和高效性。