Download or read book Intelligent Routing Via Deep Reinforcement Learning in Software Defined Network written by and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Safe QoS Aware Deep Reinforcement Learning Based Intelligent Routing for Software Defined Networks written by 吳祥勛 and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Enabling Knowledge defined Networks Deep Reinforcement Learning Graph Neural Networks and Network Analytics written by José Rafael Suárez-Varela Macià and published by . This book was released on 2020 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: Significant breakthroughs in the last decade in the Machine Learning (ML) field have ushered in a new era of Artificial Intelligence (AI). Particularly, recent advances in Deep Learning (DL) have enabled to develop a new breed of modeling and optimization tools with a plethora of applications in different fields like natural language processing, or computer vision.In this context, the Knowledge-Defined Networking (KDN) paradigm highlights the lack of adoption of AI techniques in computer networks and - as a result - proposes a novel architecture that relies on Software-Defined Networking (SDN) and modern network analytics techniques to facilitate the deployment of ML-based solutions for efficient network operation.This dissertation aims to be a step forward in the realization of Knowledge-Defined Networks. In particular, we focus on the application of AI techniques to control and optimize networks more efficiently and automatically. To this end, we identify two components within the KDN context whose development may be crucial to achieve self-operating networks in the future: (i) the automatic control module, and (ii) the network analytics platform.The first part of this thesis is devoted to the construction of efficient automatic control modules. First, we explore the application of Deep Reinforcement Learning (DRL) algorithms to optimize the routing configuration in networks. DRL has recently demonstrated an outstanding capability to solve efficiently decision-making problems in other fields. However, first DRL-based attempts to optimize routing in networks have failed to achieve good results, often under-performing traditional heuristics. In contrast to previous DRL-based solutions, we propose a more elaborate network representation that facilitates DRL agents to learn efficient routing strategies. Our evaluation results show that DRL agents using the proposed representation achieve better performance and learn faster how to route traffic in an Optical Transport Network (OTN) use case. Second, we lay the foundations on the use of Graph Neural Networks (GNN) to build ML-based network optimization tools. GNNs are a newly proposed family of DL models specifically tailored to operate and generalize over graphs of variable size and structure. In this thesis, we posit that GNNs are well suited to model the relationships between different network elements inherently represented as graphs (e.g., topology, routing). Particularly, we use a custom GNN architecture to build a routing optimization solution that - unlike previous ML-based proposals - is able to generalize well to topologies, routing configurations, and traffic never seen during the training phase.The second part of this thesis investigates the design of practical and efficient network analytics solutions in the KDN context. Network analytics tools are crucial to provide the control plane with a rich and timely view of the network state. However this is not a trivial task considering that all this information turns typically into big data in real-world networks. In this context, we analyze the main aspects that should be considered when measuring and classifying traffic in SDN (e.g., scalability, accuracy, cost). As a result, we propose a practical solution that produces flow-level measurement reports similar to those of NetFlow/IPFIX in traditional networks. The proposed system relies only on native features of OpenFlow - currently among the most established standards in SDN - and incorporates mechanisms to maintain efficiently flow-level statistics in commodity switches and report them asynchronously to the control plane. Additionally, a system that combines ML and Deep Packet Inspection (DPI) identifies the applications that generate each traffic flow.

Download or read book Quality Reliability Security and Robustness in Heterogeneous Systems written by Trung Q. Duong and published by Springer. This book was released on 2019-03-07 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of the 14th EAI International Conference on Quality, Reliability, Security and Robustness in Heterogeneous Networks, QShine 2018, held in Ho Chi Minh City, Vietnam, in December 2018. The 13 revised full papers were carefully reviewed and selected from 28 submissions. The papers are organized thematically in tracks, starting with security and privacy, telecommunication systems and networks, networks and applications.

Download or read book Applications of Reinforcement Learning to Routing and Virtualization in Computer Networks written by Soroush Haeri and published by . This book was released on 2016 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer networks and reinforcement learning algorithms have substantially advanced over the past decade. The Internet is a complex collection of inter-connected networks with a numerous of inter-operable technologies and protocols. Current trend to decouple the network intelligence from the network devices enabled by Software-Defined Networking (SDN) provides a centralized implementation of network intelligence. This offers great computational power and memory to network logic processing units where the network intelligence is implemented. Hence, reinforcement learning algorithms viable options for addressing a variety of computer networking challenges. In this dissertation, we propose two applications of reinforcement learning algorithms in computer networks. We first investigate the applications of reinforcement learning for deflection routing in buffer-less networks. Deflection routing is employed to ameliorate packet loss caused by contention in buffer-less architectures such as optical burst-switched (OBS) networks. We present a framework that introduces intelligence to deflection routing (iDef). The iDef framework decouples design of the signaling infrastructure from the underlying learning algorithm. It is implemented in the ns-3 network simulator and is made publicly available. We propose the predictive Q-learning deflection routing (PQDR) algorithm that enables path recovery and reselection, which improves the decision making ability of the node in high load conditions. We also introduce the Node Degree Dependent (NDD) signaling algorithm. The complexity of the algorithm only depends on the degree of the node that is NDD compliant while the complexity of the currently available reinforcement learning-based deflection routing algorithms depends on the size of the network. Therefore, NDD is better suited for larger networks. Simulation results show that NDD-based deflection routing algorithms scale well with the size of the network and outperform the existing algorithms. We also propose a feed-forward neural network (NN) and a feed-forward neural network with episodic updates (ENN). They employ a single hidden layer and update their weights using an associative learning algorithm. Current reinforcement learning-based deflection routing algorithms employ Q-learning, which does not efficiently utilize the received feedback signals. We introduce the NN and ENN decision-making algorithms to address the deficiency of Q-learning. The NN-based deflection routing algorithms achieve better results than Q-learning-based algorithms in networks with low to moderate loads.The second application of reinforcement learning that we consider in this dissertation is for modeling the Virtual Network Embedding (VNE) problem. We develop a VNE simulator (VNE-Sim) that is also made publicly available. We define a novel VNE objective function and prove its upper bound. We then formulate the VNE as a reinforcement learning problem using the Markov Decision Process (MDP) framework and then propose two algorithms (MaVEn-M and MaVEn-S) that employ Monte Carlo Tree Search (MCTS) for solving the VNE problem. In order to further improve the performance, we parallelize the algorithms by employing MCTS root parallelization. The advantage of the proposed algorithms is that, time permitting, they search for more profitable embeddings compared to the available algorithms that find only a single embedding solution. The simulation results show that proposed algorithms achieve superior performance.

Download or read book Future Intent Based Networking written by Mikhailo Klymash and published by Springer Nature. This book was released on 2021-12-09 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: So-called Intent-Based Networking (IBN) is founded on well-known SDN (Software-Defined Networking) and represents one of the most important emerging network infrastructure opportunities. The IBN is the beginning of a new era in the history of networking, where the network itself translates business intentions into appropriate network configurations for all devices. This minimizes manual effort, provides an additional layer of network monitoring, and provides the ability to perform network analytics and take full advantage of machine learning. The centralized, software-defined solution provides process automation and proactive problem solving as well as centralized management of the network infrastructure. With software-based network management, many operations can be performed automatically using intelligent control algorithms (artificial intelligence and machine learning). As a result, network operation costs, application response times and energy consumption are reduced, network reliability and performance are improved, network security and flexibility are enhanced. This will be a benefit for existing networks as well as evolved LTE-based mobile networks, emerging Internet of Things (IoT), Cloud systems, and soon for the future 5G/6G networks. The future networks will reach a whole new level of self-awareness, self-configuration, self-optimization, self-recovery and self-protection. This volume consists of 28 chapters, based on recent research on IBN.The volume is a collection of the most important research for the future intent-based networking deployment provided by different groups of researchers from Ukraine, Germany, Slovak Republic, Switzerland, South Korea, China, Czech Republic, Poland, Brazil, Belarus and Israel. The authors of the chapters from this collection present in depth extended research results in their scientific fields.The presented contents are highly interesting while still being rather practically oriented and straightforward to understand. Herewith we would like to wish all our readers a lot of inspiration by studying of the volume!

Download or read book Advances in Distributed Computing and Machine Learning written by Rashmi Ranjan Rout and published by Springer Nature. This book was released on 2022-07-27 with total page 712 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes a collection of peer-reviewed best selected research papers presented at the Third International Conference on Advances in Distributed Computing and Machine Learning (ICADCML 2022), organized by Department of Computer Science and Engineering, National Institute of Technology, Warangal, Telangana, India, during 15–16 January 2022. This book presents recent innovations in the field of scalable distributed systems in addition to cutting edge research in the field of Internet of Things (IoT) and blockchain in distributed environments.

Download or read book Software Defined Network Frameworks written by Mandeep Kaur and published by CRC Press. This book was released on 2024-04-22 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: Software-Defined Networks (SDN) work by virtualization of the network and the Cognitive Software-Defined Network (CSDN) combines the efficiencies of SDN with cognitive learning algorithms and enhanced protocols to automatize SDN. Partial deployment of SDN along with traditional networking devices forms a Hybrid Software-Defined Network (HSDN). Software-Defined Network Frameworks: Security Issues and Use Cases consolidates the research relating to the security in SDN, CSDN, and Hybrid SDNs. The security enhancements derived from the use of various SDN frameworks and the security challenges thus introduced, are also discussed. Overall, this book explains the different architectures of SDNs and the security challenges needed for implementing them. Features: Illustrates different frameworks of SDN and their security issues in a single volume Discusses design and assessment of efficient SDN northbound/southbound interfaces Describes cognitive computing, affective computing, machine learning, and other novel tools Illustrates coupling of SDN and traditional networking – Hybrid SDN Explores services, technologies, algorithms, and methods for data analysis in CSDN The book is aimed at researchers and graduate students in software engineering, network security, computer networks, high performance computing, communications engineering, and intelligent systems.

Download or read book Proceedings of the 4th International Conference on Telecommunications and Communication Engineering written by Maode Ma and published by Springer Nature. This book was released on 2021-09-02 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is presents the papers presented at the 4th International Conference on Telecommunications and Communication Engineering (ICTCE 2020) held on 4 -6 December, in Singapore. It covers advanced research topics in the field of computer communication and networking organized into the topics of emerging technologies of wireless communication and networks, 5G wireless communication and networks, information and network security, internet of things and fog computing. These advanced research topics are taking the lead and representing the trend of the recent academic research in the field of computer communication and networking. It is expected that the collection and publication of the research papers with the advanced topics listed in this book will further promote high standard academic research in the field and make a significant contribution to the development of economics and human society.

Download or read book An Intelligent Traffic Classification Based Optimized Routing in SDN IoT written by Isaac Ampratwum and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to speedy increase in IoT devices and its QoS requirements, providing networks solutions to meet this demand has become a major research issue. Providing fast and reliable routing paths based on the QoS requirement of IoT device is very vital. Software defined networking is one of the most current interesting development in the field of research. A new paradigm, SDN-IoT, leveraging the advantages of SDN architecture on IoT networks have been proposed to improve network quality. Also, application of artificial intelligence (AI) in SDN for traffic engineering is widely researched. In this work, we first propose a machine learning based traffic load classification into the traffic's QoS requirements. Then, a deep learning route optimization model based on the traffic classification is proposed. The model chooses the route that meets the QoS demands like latency of the identified traffic. The simulation results show that our proposed solutions perform very well and better than some significant works in the same area.

Download or read book Machine Learning Empowered Intelligent Data Center Networking written by Ting Wang and published by Springer Nature. This book was released on 2023-02-21 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to the Machine Learning Empowered Intelligent Data Center Networking Fundamentals of Machine Learning in Data Center Networks. This book reviews the common learning paradigms that are widely used in data centernetworks, and offers an introduction to data collection and data processing in data centers. Additionally, it proposes a multi-dimensional and multi-perspective solution quality assessment system called REBEL-3S. The book offers readers a solid foundation for conducting research in the field of AI-assisted data center networks. Comprehensive Survey of AI-assisted Intelligent Data Center Networks. This book comprehensively investigates the peer-reviewed literature published in recent years. The wide range of machine learning techniques is fully reflected to allow fair comparisons. In addition, the book provides in-depth analysis and enlightening discussions on the effectiveness of AI in DCNs from various perspectives, covering flow prediction, flow classification, load balancing, resource management, energy management, routing optimization, congestion control, fault management, and network security. Provides a Broad Overview with Key Insights. This book introduces several novel intelligent networking concepts pioneered by real-world industries, such as Knowledge Defined Networks, Self-Driving Networks, Intent-driven Networks and Intent-based Networks. Moreover, it shares unique insights into the technological evolution of the fusion of artificial intelligence and data center networks, together with selected challenges and future research opportunities.

Download or read book Deep Reinforcement Learning for Wireless Communications and Networking written by Dinh Thai Hoang and published by John Wiley & Sons. This book was released on 2023-06-30 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Reinforcement Learning for Wireless Communications and Networking Comprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless communication systems Deep Reinforcement Learning for Wireless Communications and Networking presents an overview of the development of DRL while providing fundamental knowledge about theories, formulation, design, learning models, algorithms and implementation of DRL together with a particular case study to practice. The book also covers diverse applications of DRL to address various problems in wireless networks, such as caching, offloading, resource sharing, and security. The authors discuss open issues by introducing some advanced DRL approaches to address emerging issues in wireless communications and networking. Covering new advanced models of DRL, e.g., deep dueling architecture and generative adversarial networks, as well as emerging problems considered in wireless networks, e.g., ambient backscatter communication, intelligent reflecting surfaces and edge intelligence, this is the first comprehensive book studying applications of DRL for wireless networks that presents the state-of-the-art research in architecture, protocol, and application design. Deep Reinforcement Learning for Wireless Communications and Networking covers specific topics such as: Deep reinforcement learning models, covering deep learning, deep reinforcement learning, and models of deep reinforcement learning Physical layer applications covering signal detection, decoding, and beamforming, power and rate control, and physical-layer security Medium access control (MAC) layer applications, covering resource allocation, channel access, and user/cell association Network layer applications, covering traffic routing, network classification, and network slicing With comprehensive coverage of an exciting and noteworthy new technology, Deep Reinforcement Learning for Wireless Communications and Networking is an essential learning resource for researchers and communications engineers, along with developers and entrepreneurs in autonomous systems, who wish to harness this technology in practical applications.

Download or read book Smart and Sustainable Technologies Rural and Tribal Development Using IoT and Cloud Computing written by Srikanta Patnaik and published by Springer Nature. This book was released on 2022-07-27 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a collection of peer-reviewed best selected research papers presented at the First International Conference on Smart and Sustainable Technologies (ICSST 2021), organized by Department of ECE, GIET University, Gunupur, Rayagada, Odisha, India, during December 16–18, 2021. The proceedings of the conference have a special focus on the developments of local tribe and rural people using smart and sustainable technologies. It is an interdisciplinary platform for researchers, practitioners, and educators as well as NGO workers who are working in the area of web engineering, IoT and cloud computing, Internet of Everything, data science, artificial intelligence, machine learning, computer vision, and intelligent robotics, particularly for the rural and tribal development.

Download or read book Communication and Intelligent Systems written by Harish Sharma and published by Springer Nature. This book was released on 2022-08-18 with total page 1213 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected research papers presented at the Third International Conference on Communication and Intelligent Systems (ICCIS 2021), organized by National institute of Technology, Delhi, India, during December 18–19, 2021. This book presents a collection of state-of-the-art research work involving cutting-edge technologies for communication and intelligent systems. Over the past few years, advances in artificial intelligence and machine learning have sparked new research efforts around the globe, which explore novel ways of developing intelligent systems and smart communication technologies. The book presents single- and multi-disciplinary research on these themes in order to make the latest results available in a single, readily accessible source.

Download or read book Proceedings of International Conference on IoT Inclusive Life ICIIL 2019 NITTTR Chandigarh India written by Maitreyee Dutta and published by Springer Nature. This book was released on 2020-04-08 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected research papers presented at the AICTE-sponsored International Conference on IoT Inclusive Life (ICIIL 2019), which was organized by the Department of Computer Science and Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, India, on December 19–20, 2019. In contributions by active researchers, the book presents innovative findings and important developments in IoT-related studies, making it a valuable resource for researchers, engineers, and industrial professionals around the globe.

Download or read book Emerging Trends and Applications in Artificial Intelligence written by Fausto Pedro García Márquez and published by Springer Nature. This book was released on with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Software Defined Networking for Ad Hoc Networks written by Mangesh M. Ghonge and published by Springer Nature. This book was released on 2022-02-09 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensive overview of Software-Defined Network (SDN) based ad-hoc network technologies and exploits recent developments in this domain, with a focus on emerging technologies in SDN based ad-hoc networks. The authors offer practical and innovative applications in Network Security, Smart Cities, e-health, and Intelligent Systems. This book also addresses several key issues in SDN energy-efficient systems, the Internet of Things, Big Data, Cloud Computing and Virtualization, Machine Learning, Deep Learning, and Cryptography. The book includes different ad hoc networks such as MANETs and VANETs, along with a focus on evaluating and comparing existing SDN-related research on various parameters. The book provides students, researchers, and practicing engineers with an expert guide to the fundamental concepts, challenges, architecture, applications, and state-of-the-art developments in the field.