Journal ArticleDOI
Machine Learning-Based Multipath Routing for Software Defined Networks
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TLDR
In this paper, a machine learning-based multipath routing (MLMR) framework is proposed for software-defined networks with quality-of-service (QoS) constraints and flow rules space constraints.Abstract:
Network softwarization has recently been enabled via the software-defined networking (SDN) paradigm, which separates the data plane from control plane allowing for a flexible and centralized control of networks. This separation facilitates implementation of machine learning techniques for network management and optimization. In this work, a machine learning-based multipath routing (MLMR) framework is proposed for software-defined networks with quality-of-service (QoS) constraints and flow rules space constraints. The QoS-aware multipath routing problem in SDN is modeled as multicommodity network flow problem with side constraints, that is known to be NP-hard. The proposed framework utilizes network status estimates, and their corresponding routing configurations available at the network central controller to learn a mapping function between them. Once the mapping function is learned, it is applied on live-inputs of network status and routing requests to predict a multipath routing solutions in real-time. Performance evaluations of the MLMR framework on real traces of network traffic verify its accuracy and resilience to noise in training data. Furthermore, the MLMR framework demonstrates more than 98.99% improvement in computational efficiency.read more
Citations
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Journal ArticleDOI
A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges
Imran,Zeba Ghaffar,Abdullah Alshahrani,Muhammad Fayaz,Ahmed Mohammed Alghamdi,Jeonghwan Gwak +5 more
TL;DR: A topical survey of the application and impact of software-defined networking on the Internet of things networks, carried out from the different perspectives ofSoftware-based Internet of Things networks, including wide-area networks, edge networks, and access networks.
Journal ArticleDOI
A Survey on Machine Learning Techniques for Routing Optimization in SDN
TL;DR: In this paper, the authors present a survey of machine learning techniques for routing optimization in SDN based on three core categories (i.e., supervised learning, unsupervised learning, and reinforcement learning).
Journal ArticleDOI
A comprehensive survey of DDoS defense solutions in SDN: Taxonomy, research challenges, and future directions
TL;DR: A systematic literature review on various DDoS defense mechanisms to protect the control plane, data plane, and data-control plane communication channel and presents the taxonomy of DDoSDefense solutions that classify the reviewed articles based on the attack targets, DDoSdefense approaches, testing environment, and traffic generation mechanism.
Journal ArticleDOI
DynamicTuple: The dynamic adaptive tuple for high-performance packet classification
TL;DR: In this paper, the Dynamic Adaptive Tuple (DynamicTuple) is proposed for both fast packet classification and rule updating simultaneously, which exploits dynamic programming to find the appropriate tuple formulation to minimize the lookup time.
Journal ArticleDOI
Intelligent Secure Networking in In-band Full-duplex Dynamic Access Networks: Spectrum Management and Routing Protocol
Haythem Bany Salameh,Haythem Bany Salameh,Haythem Bany Salameh,Zainab Khader,Ahmad Al Ajlouni +4 more
TL;DR: In this article, a secure-aware IBFD-based routing protocol is proposed to mitigate the effects of jamming attacks on cognitive radio (CR) systems by considering the unique characteristics of the CRN environment.
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