期刊
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
卷 35, 期 10, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jksuci.2023.101809
关键词
Faulty switch handling; Time base routing; Artificial Intelligence (AI); Preventive maintenance; SDN; Traditional networking; Operation and maintenance switch failure; Network reliability and availability; Network management
This study introduces a Preventive Maintenance Routing Framework (PMRF) for uninterrupted upgrades in SDN settings. By selecting reliable backup paths and timely isolating maintenance switches, the framework reduces packet drops, enhances SDN operation efficiency, and efficiently manages TCAM resources.
In the realm of software-defined networking (SDN), ensuring robust maintenance operations and the consistent performance of switch operations, especially during maintenance and upgrades, is of paramount importance. Traditional SDN methods, both reactive and proactive, face challenges like extended convergence times, inefficient use of Ternary Content-Addressable Memory (TCAM) space, and unpredictable maintenance time. To tackle these issues and enhance maintenance operations, this paper introduces the Preventive Maintenance Routing Framework (PMRF) - a novel approach designed for uninterrupted upgrades in SDN settings. Our framework focuses on selecting reliable backup paths and timely isolation of the maintenance switch except critical switch, framed as an NP-hard mixed-integer linear programming problem. To address this complexity, we present a multi-objective heuristic algorithm that efficiently determines the best path for upcoming affected flows. Testing shows that, in varied network conditions, PMRF markedly reduced packet drops, potentially enhancing SDN operation efficiency, minimizing end-to-end delays, and maintaining failure recovery times below 50 ms and efficiently manage TCAM resources. In summary, PMRF offers a robust solution for optimizing network reliability and efficiency during maintenance operations and upgrades in core networks.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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