A consistent QoS routing strategy for video streaming services in SDN networks

Henni, Djamel Eddine; Ghomari, Abdelghani; Hadjadj‐Aoul, Yassine

doi:10.1002/dac.4177

Cited by 18 publications

(7 citation statements)

References 24 publications

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“…Furthermore, the control plane can enforce QoS policies to prioritize certain traffic over others. For instance, video traffic can be given higher priority than file transfer traffic, as video traffic is time-critical, in order to increase the overall quality of experience of the user [392]. The control plane should ensure that traffic is rerouted in the event of a network failure.…”

Section: Traffic Engineeringmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks.

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Section: Traffic Engineeringmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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“…For the north-south interface, the southbound interface is related to format conversion and data transmission, which supports di erent types of protocols. And northbound interface adopts RESTful protocol to expand interactive interfaces [14][15][16][17]. e speci c structure is shown in Figure 1.…”

Section: The Odl Centralized Control Framework In This Papermentioning

confidence: 99%

ODL Centralized Control of Power Communication Network Based on Bio-Inspired Algorithms and SDN

Min

2022

Mathematical Problems in Engineering

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An ODL centralized control strategy is designed to study the problem of heavy load in the power communication network, in which the machine learning method is introduced. SDN technology is used to establish SDN cluster control structure, and different algorithms, such as genetic algorithm, are utilized to optimize resource scheduling. The results show that the improved algorithm obtains the shortest link path through 28 iterations. At the same time, AHP is used to switch the network spectrum. Moreover, the application effect of the control strategy is simulated and analyzed, and its effect on network communication application is verified.

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“…39 The second module uses OpenFlow messages to collect port statistics. Links's cost values are uniformly distributed within an interval [1,15] as used in Juttner et al 14 where LARAC is proposed. For BiLAD, we try to use the same configuration as in Kou et al 28 We set parameter γ ¼ 0:05, and ϵ ¼ π 9 ¼ 20 o ≈ 0:349.…”

Section: Experiments Setupmentioning

confidence: 99%

“…Software‐Defined Networking (SDN) is a fast emerging paradigm that offers abstraction, programmability, and greater central network control flexibility compared to traditional networking. It promises to provide services with a QoS guarantee while efficiently utilizing the network resources and maintain QoS consistency, 1 more specifically for killer services such as audio and video streaming, online gaming, 2 or for critical delay‐sensitive applications as in medical (e.g., Telesurgery) 3 or industrial environments 4 5 …”

Section: Introductionmentioning

confidence: 99%

Bootstrapped LARAC algorithm for fast delay‐sensitive QoS provisioning in SDN networks

BinSahaq

Sheltami

Mahmoud

2021

Int J Communication

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In today's networks, QoS provisioning becomes a major concern for service providers due to the massive increase in connected devices (e.g., mobile devices, servers, and Things), traffic in between, and the diverse service requirements (e.g., delay and bandwidth). Software-Defined Networking (SDN) promises solution for such issues with its central network control flexibility compared to traditional networking; nonetheless, a design of a fast and QoS guaranteeing routing algorithm is still needed. Delay Constrained Least Cost (DCLC) is a well-known NP-hard problem. LAgrange Relaxation-based Aggregated Cost (LARAC) algorithm is a Dijkstra-based QoS-aware algorithm and one of many heuristic algorithms proposed in the literature to solve the DCLC problem.Despite its outstanding performance over other algorithms in SDN, it still needs improvement. In this article, we present a modified version of LARAC, called MODLARAC, to improve the solution feasibility search state by exploiting LAR-AC's lower-bound paths before the approximation process start. Also, we modify its stop condition to avoid extra non-useful Dijkstra calls. We implement and evaluate MODLARAC using a realistic ISP network topology using Mininet and controlled by Floodlight SDN controller. We then compare MODLARAC against the original LARAC and BiLAD algorithms. The obtained results showed improvement up to 20% reduction in Dijkstra calls count compared to original LARAC and BiLAD algorithms without a significant increase in path's cost or delay metrics. It reached only 3% increase in path cost and 7% in path delay in its worst case with a safe distance of 11% lower than the delay demand.

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A consistent QoS routing strategy for video streaming services in SDN networks

Cited by 18 publications

References 24 publications

A Comprehensive Survey on Knowledge-Defined Networking

A Comprehensive Survey on Knowledge-Defined Networking

ODL Centralized Control of Power Communication Network Based on Bio-Inspired Algorithms and SDN

Bootstrapped LARAC algorithm for fast delay‐sensitive QoS provisioning in SDN networks

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