Challenges and Solutions for hybrid SDN

Rojas, Elisa; Amin, Rashid; Guerrero, Carmen; Savi, Marco; Rastegarnia, Adib

doi:10.1016/j.comnet.2021.108198

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…We summarised the main features of each model in Table 1: The current trend shows that in-band seems to be prominently used in SDN deployments [10], particularly for large networks in which the cost of using out-of-band might be exorbitant. Additionally, in-band control allows a wide range of new applications, particularly for constrained and hybrid SDN environments [11,12], in which out-of-band control is complex (or even impossible) to deploy. Some of the use cases leveraging in-band control include 5G [13] and Non-Terrestrial Networks (NTNs) [14], Internet of Things (IoT) (e.g., underwater networks [15], energy saving [16] or resource-constrained environments [17]), emergency and disaster situations [18,19], and enhanced network security [20].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey of In-Band Control in SDN: Challenges and Opportunities

et al. 2023

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Software-Defined Networking (SDN) is a thriving networking architecture that has gained popularity in recent years, particularly as an enabling technology to foster paradigms like edge computing. SDN separates the control and data planes, which are later on synchronised via a control protocol such as OpenFlow. In-band control is a type of SDN control plane deployment in which the control and data planes share the same physical network. It poses several challenges, such as security vulnerabilities, network congestion, or data loss. Nevertheless, despite these challenges, in-band control also presents significant opportunities, including improved network flexibility and programmability, reduced costs, and increased reliability. Benefiting from the previous advantages, diverse in-band control designs exist in the literature, with the objective of improving the operation of SDN networks. This paper surveys the different approaches that have been proposed so far towards the advance in in-band SDN control, based on four main categories: automatic routing, fast failure recovery, network bootstrapping, and distributed control. Across these categories, detailed summary tables and comparisons are presented, followed by a discussion on current trends a challenges in the field. Our conclusion is that the use of in-band control in SDN networks is expected to drive innovation and growth in the networking industry, but efforts for holistic and full-fledged proposals are still needed.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey of In-Band Control in SDN: Challenges and Opportunities

et al. 2023

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“…Recently, SDN technology has become widely popular in network development research. SDN programmability implies efficient network application, bringing application benefits to the entire network-from providers to end users [5,6]. SDNs are used to produce a logical view of the entire network (i.e., a network abstract, which is the responsibility of the control plane; meanwhile, traffic forwarding is managed at the data plane [7].…”

Section: Introductionmentioning

confidence: 99%

Software-Defined Networking-Based Campus Networks Via Deep Reinforcement Learning Algorithms: The Case of University of Technology

Ali Sadiq Salim

2023

IJRITCC

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As a consequence of the COVID-19 pandemic, networks need to be adopted to satisfy the new situation. People have been introduced to new modes of working from home, attending teleconferences, and taking part in e-learning. Other technologies, including smart cities, the Internet of Things, and simulation tools, have also seen a rise in demand. In the new situation, the network most affected is the campus network. Fortunately, a powerful and flexible network model called the software-defined network (SDN) is currently being standardized. SDN can significantly improve the performance of campus networks. Consequently, many scholars and experts have focused on enhancing campus networks via SDN technology. Integrating deep reinforcement learning (DRL) with SDN is pivotal for advancing the quality of service (QoS) of contemporary networks. Their integration enables real-time collaboration, intelligent decision making, and optimized traffic flow and resource allocation. The system proposed in this research is a DRL algorithm applied to a campus network—the University of Technology—and investigated as a case study. The proposed system employs a two-method approach for optimizing the QoS of a network. First, the system classifies service types and directs TCP traffic by using a deep Q-network (DQN) for intelligent routing; then, UDP traffic is managed using the Dijkstra algorithm for shortest-path selection. This hybrid model leverages the strengths of machine learning and classical algorithms to ensure efficient resource allocation and high-quality data transmission. The system combines the adaptability of DQN with the proven reliability of the Dijkstra algorithm to enhance dynamically the network performance. The proposed hybrid system, which used DQN for TCP traffic and the Dijkstra algorithm for UDP traffic, was benchmarked against two other algorithms. The first algorithm was an advanced version of the Dijkstra algorithm that was designed specifically for this study. The second algorithm involved a Q-learning (QL)-based approach. The evaluation metrics included throughput and latency. Tests were conducted under various topologies and load conditions. The research findings revealed a clear advantage of the hybrid system in complex network topologies under heavy-load conditions. The throughput of the proposed system was 30% higher than the advanced Dijkstra and QL algorithms. The latency benefits were pronounced, with a 50% improvement over the competing algorithms.

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“…For instance, it is crucial to seamlessly integrate wired and wireless communication links. For that reason, all techniques and protocols need to embrace this diversity in their design principles, and following a progressive hybrid SDN [120] deployment might be desirable.…”

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confidence: 99%

A Survey on AI-Empowered Softwarized Industrial IoT Networks

Rojas,

Carrascal,

Lopez-Pajares

et al. 2024

Electronics

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The future generation of mobile networks envision Artificial Intelligence (AI) and the Internet of Things (IoT) as key enabling technologies that will foster the emergence of sophisticated use cases, with the industrial sector being one to benefit the most. This survey reviews related works in this field, with a particular focus on the specific role of network softwarization. Furthermore, the survey delves into their context and trends, categorizing works into several types and comparing them based on their contribution to the advancement of the state of the art. Since our analysis yields a lack of integrated practical implementations and a potential desynchronization with current standards, we finalize our study with a summary of challenges and future research ideas.

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Challenges and Solutions for hybrid SDN

Cited by 9 publications

References 26 publications

A Comprehensive Survey of In-Band Control in SDN: Challenges and Opportunities

A Comprehensive Survey of In-Band Control in SDN: Challenges and Opportunities

Software-Defined Networking-Based Campus Networks Via Deep Reinforcement Learning Algorithms: The Case of University of Technology

A Survey on AI-Empowered Softwarized Industrial IoT Networks

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