دورية أكاديمية
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SFC active reconfiguration based on user mobility and resource demand prediction in dynamic IoT-MEC networks.

التفاصيل البيبلوغرافية
العنوان: SFC active reconfiguration based on user mobility and resource demand prediction in dynamic IoT-MEC networks.
المؤلفون: Guo S; Chongqing College of Mobile Communication, Qijiang, Chongqing, China.; Chongqing Key Laboratory of Public Big Data Security Technology, Qijiang, Chongqing, China., Liu L; School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China., Jing T; School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China., Liu H; Department of Orthopedics, The Second Affiliated Hospital of Army Medical University, Chongqing, China.
المصدر: PloS one [PLoS One] 2024 Aug 01; Vol. 19 (8), pp. e0306777. Date of Electronic Publication: 2024 Aug 01 (Print Publication: 2024).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science
مواضيع طبية MeSH: Internet of Things* , Algorithms*, Computer Communication Networks ; Humans
مستخلص: To achieve secure, reliable, and scalable traffic delivery, request streams in mobile Internet of Things (IoT) networks supporting Multi-access Edge Computing (MEC) typically need to pass through a service function chain (SFC) consisting of an ordered series of Virtual Network Functions (VNFs), and then arrive at the target application in the MEC for processing. The high mobility of users and the real-time variability of network traffic in IoT-MEC networks lead to constant changes in the network state, which results in a mismatch between the performance requirements of the currently deployed SFCs and the allocated resources. Meanwhile, there are usually multiple instances of the same VNF in the network, and proactively reconfiguring the deployed SFCs based on the network state changes to ensure high quality of service in the network is a great challenge. In this paper, we study the SFC Reconfiguration Strategy (SFC-RS) based on user mobility and resource demand prediction in IoT MEC networks, aiming to minimize the end-to-end delay and reconfiguration cost of SFCs. First, we model SFC-RS as Integer Linear Programming (ILP). Then, a user trajectory prediction model based on codec movement with attention mechanism and a VNF resource demand prediction model based on the Long Short-Term Memory (LSTM) network are designed to accurately predict user trajectories and node computational and storage resources, respectively. Based on the prediction results, a Prediction-based SFV Active Reconfiguration (PSAR) algorithm is proposed to achieve seamless SFC migration and routing update before the user experience quality degrades, ensuring network consistency and high quality service. Simulation results show that PSAR provides 51.28%, 28.60%, 21.75%, and 16.80% performance improvement over the existing TSRFCM, DDQ, OSA, and DPSM algorithms in terms of end-to-end delay reduction, and 33.32%, 18.94%, 67.42%, and 60.61% performance optimization in terms of reconfiguration cost reduction.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Guo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
تواريخ الأحداث: Date Created: 20240801 Date Completed: 20240801 Latest Revision: 20240803
رمز التحديث: 20240803
مُعرف محوري في PubMed: PMC11293735
DOI: 10.1371/journal.pone.0306777
PMID: 39088548
قاعدة البيانات: MEDLINE
الوصف
تدمد:1932-6203
DOI:10.1371/journal.pone.0306777