Data plane failure and its recovery techniques in SDN: A systematic literature review

Khan, Nauman Ali; Salleh, Rosli; Koubâa, Anis; Khan, Zahid; Khan, Muhammad Khurram; Ali, Ihsan

doi:10.1016/j.jksuci.2023.02.001

Cited by 10 publications

(7 citation statements)

References 92 publications

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“…This average was derived by executing each algorithm 100 consecutive times and then calculating the mean response time from the total accumulated data. This process, therefore, ensures the reliability and consistency of the results, acknowledging that a single run can be susceptible to anomalous external influences [57].…”

Section: Resultsmentioning

confidence: 93%

Development of an Intelligent Service Delivery System to Increase Efficiency of Software Defined Networks

Joldasbayev,

Sapakova,

Zhaksylyk

et al. 2023

IJACSA

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The burgeoning complexity in network management has garnered considerable attention, specifically focusing on Software-Defined Networking (SDN), a transformative technology that addresses limitations inherent in traditional network infrastructures. Despite its advantages, SDN is often susceptible to bottlenecks and excessive load issues, underscoring the necessity for more robust load balancing solutions. Previous research in this realm has predominantly concentrated on employing static or dynamic methodologies, encapsulating only a handful of parameters for traffic management, thereby limiting their effectiveness. This study introduces an innovative, intelligence-led approach to service delivery systems in SDN, specifically by orchestrating packet forwarding-encompassing both TCP and UDP traffic-through a multi-faceted analysis utilizing twelve distinct parameters elaborated in subsequent sections. This research leverages advanced machine learning algorithms, notably K-Means and DBSCAN clustering, to discern patterns and optimize traffic distribution, ensuring a more nuanced, responsive load balancing mechanism. A salient feature of this methodology involves determining the ideal number of operational clusters to enhance efficiency systematically. The proposed system underwent rigorous testing with an escalating scale of network packets, encompassing counts of 5,000 to an extensive 10,000,000, to validate performance under varying load conditions. Comparative analysis between K-Means and DBSCAN's results reveals critical insights into their operational efficacy, corroborated by juxtaposition with extant scholarly perspectives. This investigation's findings significantly contribute to the discourse on adaptive network solutions, demonstrating that an intelligent, parameter-rich approach can substantively mitigate load-related challenges, thereby revolutionizing service delivery paradigms within Software-Defined Networks.

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Section: Resultsmentioning

confidence: 93%

Development of an Intelligent Service Delivery System to Increase Efficiency of Software Defined Networks

Joldasbayev,

Sapakova,

Zhaksylyk

et al. 2023

IJACSA

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“…Encompasses engaging with specialized vendors or service providers who offer dedicated recovery services and facilities [78]- [80]. These vendors provide ready-to-use recovery infrastructure [81] and expertise to assist in the recovery process.…”

Section: Vendor Recovery Servicesmentioning

confidence: 99%

Ensuring resilience: Integrating IT disaster recovery planning and business continuity for sustainable information technology operations

Derick Musundi Kesa

2023

World J. Adv. Res. Rev.

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Information Technology (IT) Disaster Recovery Planning (IT DRP) and Business Continuity (BC) are essential components of an organization’s overall resilience strategy. IT DRP focuses on the recovery and restoration of IT systems, infrastructure, and services in the event of a disruptive incident or disaster, aiming to minimize downtime and data loss. BC, on the other hand, encompasses a broader perspective, addressing the organization's ability to maintain essential operations and deliver critical services during and after a disruption. This paper provides an overview of IT DRP and BC, highlighting their importance, challenges, and strategies. It also identifies the research gaps and future research scope in these areas. The findings indicate that both IT DRP and BC face challenges in their effective implementation. These challenges include the evolving nature of technology, increasing complexity of IT systems, budget constraints, organizational resistance to change, and the need for skilled personnel. Overcoming these challenges requires a comprehensive understanding of the organization’s IT infrastructure, risk assessment, and the development of robust recovery strategies and plans. It is also noted that despite considerable research on IT DRP and BC, there are several research gaps that deserve attention. These include the development of advanced technologies and tools for more efficient recovery and continuity, the integration of IT DRP and BC with overall organizational risk management, the impact of emerging technologies such as cloud computing and virtualization on recovery strategies, and the evaluation of the effectiveness and cost-efficiency of different IT DRP and BC strategies.

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“…A comparative analysis with traditional networking paradigms reveals the transformative nature of SDN. Traditional networks exhibit tight coupling between the data and control planes within network devices, resulting in less agile and more complex infrastructure [9]. By contrast, SDN decouples these planes, providing a more agile and adaptable network environment [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Security Control and Data Planes of SDN: A Comprehensive Review of Traditional, AI, and MTD Approaches to Security Solutions

Abdi,

Audah,

Salh

et al. 2024

IEEE Access

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Software-Defined Networking (SDN) is a groundbreaking technology that has transformed network management significantly. By integrating data and control, SDN offers unparalleled flexibility and responsiveness, thereby overcoming the limitations of conventional network architectures. However, a centralized controller, which is a hallmark of SDN, is a double-edged security sword that offers easy control. This also becomes a dangerous point of failure for the entire network. To the best of our knowledge, this is the first comprehensive study to explore traditional-based, artificial intelligence (AI)-based, and moving target defense (MTD) approaches to securing SDN. The study begins with a survey of traditional security solutions for SDN, encompassing authentication, authorization, encryption, security protocols, firewalls, and flow verification, by addressing security threats and vulnerabilities in both data and control planes. The study then investigates the application of AI-based security solutions in an SDN environment, focusing on how Machine Learning (ML) and Deep Learning (DL) techniques are leveraged to address advanced security threats. Additionally, the survey examines MTD mechanisms within data and control plane security. Several in-depth techniques, including the randomization of Internet Protocol (IP) and Media Access Control (MAC) addresses, port numbers, and flow tables, and delving into the relationship between security threats, MTD strategies, and the specific controllers employed in experimental implementations. We utilized the widely recognized STRIDE cybersecurity framework to systematically identify and evaluate the potential threats to SDN security. Our analysis resulted in a comprehensive list of security challenges, and we propose future research directions aimed at addressing emerging threats in both the data and control planes.

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Data plane failure and its recovery techniques in SDN: A systematic literature review

Cited by 10 publications

References 92 publications

Development of an Intelligent Service Delivery System to Increase Efficiency of Software Defined Networks

Development of an Intelligent Service Delivery System to Increase Efficiency of Software Defined Networks

Ensuring resilience: Integrating IT disaster recovery planning and business continuity for sustainable information technology operations

Security Control and Data Planes of SDN: A Comprehensive Review of Traditional, AI, and MTD Approaches to Security Solutions

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