An Approach Based on Knowledge-Defined Networking for Identifying Video Streaming Flows in 5G Networks

Herrera, Luis Miguel Castañeda; Duque-Torres, Alejandra; Muñoz, Wilmar Yesid Campo

doi:10.1109/tla.2021.9477274

Cited by 6 publications

(4 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rafiq et al proposed an autonomous driving system based on KDN to achieve optimal path selection for deploying service function chaining and reactive traffic routing between edge clouds [38]. Herrera et al proposed that the utilization of the KDN architecture can enhance the control and management of network resources, and identify video streaming services when data traffic increases, thus ensuring network performance [39].…”

Section: Related Workmentioning

confidence: 99%

Knowledge-Defined Edge Computing Networks Assisted Long-Term Optimization of Computation Offloading and Resource Allocation Strategy

Yang,

Wang,

et al. 2024

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

With the proliferation of devices connected to the Internet of Things (IoT), the complexity of network management has increased. To intelligently manage large-scale networks, we propose a Knowledge-Defined Edge Computing Networks (KDECN) architecture. Edge Nodes (ENs) deployed in the KDECN architecture are responsible for collecting and preprocessing the relevant information uploaded by User Devices (UDs), and provide computation resources for UDs. Futhermore, since multiple UDs share system computation resources, one computing decision will affect the subsequent decision-making of other UDs. Thus, accurately predicting the demands for UD task requests is a key challenge to maximize long-term execution utility. To this end, we deploy the LSTM-based Task Request Demand Prediction (TRDP) method on the management plane of KDECN architecture to predict the task request quantity of UDs in each future time slot. In order to maximize long-term execution utility of the system, we propose a Deep Reinforcement Learning (DRL)based Long-term Computation Offloading and computation Resource Allocation (L-CORA) algorithm. Specifically, the proposed L-CORA algorithm makes computing decisions based on the prediction of the offloading task quantity and the personalized demands of UDs to ensure the long-term quality of computing service. Extensive experiments with Shanghai real-world datasets to prove that the KDECN-based L-CORA algorithm effectively improves the average utility of the system.

show abstract

Section: Related Workmentioning

confidence: 99%

Knowledge-Defined Edge Computing Networks Assisted Long-Term Optimization of Computation Offloading and Resource Allocation Strategy

Yang,

Wang,

et al. 2024

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

show abstract

“…A framework for identifying heavy-hitter flows using machine learning in KDNs has been investigated in [47]. ML has also been used for video flow classification in 5G KDNs [48]. However, the KDN concept is still very pre-mature, and it lacks standardization and protocols for intra-plane communication [49].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

View full text Add to dashboard Cite

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.

show abstract

“…In [56], an artificial intelligence-based approach to identifying huge striking fluxes was examined. On top of that, fifth-generation KDNs [57] have employed artificial intelligence for audiovisual traffic categorization. Machine learning-specifically, deep learning-has been extensively applied in healthcare networks for medical image analysis, etc.…”

Section: Introductionmentioning

confidence: 99%

A Review of Blockchain Technology in Knowledge-Defined Networking, Its Application, Benefits, and Challenges

Wijesekara,

Gunawardena

2023

Network

View full text Add to dashboard Cite

Knowledge-Defined Networking (KDN) necessarily consists of a knowledge plane for the generation of knowledge, typically using machine learning techniques, and the dissemination of knowledge, in order to make knowledge-driven intelligent network decisions. In one way, KDN can be recognized as knowledge-driven Software-Defined Networking (SDN), having additional management and knowledge planes. On the other hand, KDN encapsulates all knowledge-/intelligence-/ cognition-/machine learning-driven networks, emphasizing knowledge generation (KG) and dissemination for making intelligent network decisions, unlike SDN, which emphasizes logical decoupling of the control plane. Blockchain is a technology created for secure and trustworthy decentralized transaction storage and management using a sequence of immutable and linked transactions. The decision-making trustworthiness of a KDN system is reliant on the trustworthiness of the data, knowledge, and AI model sharing. To this point, a KDN may make use of the capabilities of the blockchain system for trustworthy data, knowledge, and machine learning model sharing, as blockchain transactions prevent repudiation and are immutable, pseudo-anonymous, optionally encrypted, reliable, access-controlled, and untampered, to protect the sensitivity, integrity, and legitimacy of sharing entities. Furthermore, blockchain has been integrated with knowledge-based networks for traffic optimization, resource sharing, network administration, access control, protecting privacy, traffic filtering, anomaly or intrusion detection, network virtualization, massive data analysis, edge and cloud computing, and data center networking. Despite the fact that many academics have employed the concept of blockchain in cognitive networks to achieve various objectives, we can also identify challenges such as high energy consumption, scalability issues, difficulty processing big data, etc. that act as barriers for integrating the two concepts together. Academicians have not yet reviewed blockchain-based network solutions in diverse application categories for diverse knowledge-defined networks in general, which consider knowledge generation and dissemination using various techniques such as machine learning, fuzzy logic, and meta-heuristics. Therefore, this article fills a void in the content of the literature by first reviewing the diverse existing blockchain-based applications in diverse knowledge-based networks, analyzing and comparing the existing works, describing the advantages and difficulties of using blockchain systems in KDN, and, finally, providing propositions based on identified challenges and then presenting prospects for the future.

show abstract

An Approach Based on Knowledge-Defined Networking for Identifying Video Streaming Flows in 5G Networks

Cited by 6 publications

References 32 publications

Knowledge-Defined Edge Computing Networks Assisted Long-Term Optimization of Computation Offloading and Resource Allocation Strategy

Knowledge-Defined Edge Computing Networks Assisted Long-Term Optimization of Computation Offloading and Resource Allocation Strategy

A Comprehensive Survey on Knowledge-Defined Networking

A Review of Blockchain Technology in Knowledge-Defined Networking, Its Application, Benefits, and Challenges

Contact Info

Product

Resources

About