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  • Open Access

    REVIEW

    A Survey of Link Failure Detection and Recovery in Software-Defined Networks

    Suheib Alhiyari, Siti Hafizah AB Hamid*, Nur Nasuha Daud

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 103-137, 2025, DOI:10.32604/cmc.2024.059050 - 03 January 2025

    Abstract Software-defined networking (SDN) is an innovative paradigm that separates the control and data planes, introducing centralized network control. SDN is increasingly being adopted by Carrier Grade networks, offering enhanced network management capabilities than those of traditional networks. However, because SDN is designed to ensure high-level service availability, it faces additional challenges. One of the most critical challenges is ensuring efficient detection and recovery from link failures in the data plane. Such failures can significantly impact network performance and lead to service outages, making resiliency a key concern for the effective adoption of SDN. Since the More >

  • Open Access

    ARTICLE

    A Decentralized and TCAM-Aware Failure Recovery Model in Software Defined Data Center Networks

    Suheib Alhiyari, Siti Hafizah AB Hamid*, Nur Nasuha Daud

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1087-1107, 2025, DOI:10.32604/cmc.2024.058953 - 03 January 2025

    Abstract Link failure is a critical issue in large networks and must be effectively addressed. In software-defined networks (SDN), link failure recovery schemes can be categorized into proactive and reactive approaches. Reactive schemes have longer recovery times while proactive schemes provide faster recovery but overwhelm the memory of switches by flow entries. As SDN adoption grows, ensuring efficient recovery from link failures in the data plane becomes crucial. In particular, data center networks (DCNs) demand rapid recovery times and efficient resource utilization to meet carrier-grade requirements. This paper proposes an efficient Decentralized Failure Recovery (DFR) model… More >

  • Open Access

    ARTICLE

    Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

    Mohamed Zarouan1, Ibrahim M. Mehedi1,2,*, Shaikh Abdul Latif3, Md. Masud Rana4

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1341-1364, 2024, DOI:10.32604/cmes.2023.030037 - 17 November 2023

    Abstract Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamless operation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0. Specifically, various modernized industrial processes have been equipped with quite a few sensors to collect process-based data to find faults arising or prevailing in processes along with monitoring the status of processes. Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Due to the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional… More >

  • Open Access

    ARTICLE

    Prediction of Link Failure in MANET-IoT Using Fuzzy Linear Regression

    R. Mahalakshmi1,*, V. Prasanna Srinivasan2, S. Aghalya3, D. Muthukumaran4

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1627-1637, 2023, DOI:10.32604/iasc.2023.032709 - 05 January 2023

    Abstract A Mobile Ad-hoc NETwork (MANET) contains numerous mobile nodes, and it forms a structure-less network associated with wireless links. But, the node movement is the key feature of MANETs; hence, the quick action of the nodes guides a link failure. This link failure creates more data packet drops that can cause a long time delay. As a result, measuring accurate link failure time is the key factor in the MANET. This paper presents a Fuzzy Linear Regression Method to measure Link Failure (FLRLF) and provide an optimal route in the MANET-Internet of Things (IoT). This… More >

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