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Bringing Machine Learning To Software Defined Networks

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Bringing Machine Learning To Software Defined Networks

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Author : Zehua Guo
language : en
Publisher:
Release Date : 2022

Bringing Machine Learning To Software Defined Networks written by Zehua Guo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Electronic books categories.

Emerging machine learning techniques bring new opportunities to flexible network control and management. This book focuses on using state-of-the-art machine learning-based approaches to improve the performance of Software-Defined Networking (SDN). It will apply several innovative machine learning methods (e.g., Deep Reinforcement Learning, Multi-Agent Reinforcement Learning, and Graph Neural Network) to traffic engineering and controller load balancing in software-defined wide area networks, as well as flow scheduling, coflow scheduling, and flow migration for network function virtualization in software-defined data center networks. It helps readers reflect on several practical problems of deploying SDN and learn how to solve the problems by taking advantage of existing machine learning techniques. The book elaborates on the formulation of each problem, explains design details for each scheme, and provides solutions by running mathematical optimization processes, conducting simulated experiments, and analyzing the experimental results.

Bringing Machine Learning To Software Defined Networks

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Author : Zehua Guo
language : en
Publisher: Springer Nature
Release Date : 2022-10-05

Bringing Machine Learning To Software Defined Networks written by Zehua Guo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-05 with Computers categories.

Developing Networks Using Artificial Intelligence

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Author : Haipeng Yao
language : en
Publisher: Springer
Release Date : 2019-04-26

Developing Networks Using Artificial Intelligence written by Haipeng Yao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-26 with Technology & Engineering categories.

This book mainly discusses the most important issues in artificial intelligence-aided future networks, such as applying different ML approaches to investigate solutions to intelligently monitor, control and optimize networking. The authors focus on four scenarios of successfully applying machine learning in network space. It also discusses the main challenge of network traffic intelligent awareness and introduces several machine learning-based traffic awareness algorithms, such as traffic classification, anomaly traffic identification and traffic prediction. The authors introduce some ML approaches like reinforcement learning to deal with network control problem in this book. Traditional works on the control plane largely rely on a manual process in configuring forwarding, which cannot be employed for today's network conditions. To address this issue, several artificial intelligence approaches for self-learning control strategies are introduced. In addition, resource management problems are ubiquitous in the networking field, such as job scheduling, bitrate adaptation in video streaming and virtual machine placement in cloud computing. Compared with the traditional with-box approach, the authors present some ML methods to solve the complexity network resource allocation problems. Finally, semantic comprehension function is introduced to the network to understand the high-level business intent in this book. With Software-Defined Networking (SDN), Network Function Virtualization (NFV), 5th Generation Wireless Systems (5G) development, the global network is undergoing profound restructuring and transformation. However, with the improvement of the flexibility and scalability of the networks, as well as the ever-increasing complexity of networks, makes effective monitoring, overall control, and optimization of the network extremely difficult. Recently, adding intelligence to the control plane through AI&ML become a trend and a direction of network development This book's expected audience includes professors, researchers, scientists, practitioners, engineers, industry managers, and government research workers, who work in the fields of intelligent network. Advanced-level students studying computer science and electrical engineering will also find this book useful as a secondary textbook.

Special Topics In Information Technology

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Author : Angelo Geraci
language : en
Publisher: Springer Nature
Release Date : 2021-02-26

Special Topics In Information Technology written by Angelo Geraci and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-26 with Technology & Engineering categories.

This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2019-20 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists.

Future Intent Based Networking

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Author : Mikhailo Klymash
language : en
Publisher: Springer Nature
Release Date : 2021-12-09

Future Intent Based Networking written by Mikhailo Klymash and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-09 with Technology & Engineering categories.

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!

Information Centric Networks Icn

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Author : Nitul Dutta
language : en
Publisher: Springer
Release Date : 2022-08-29

Information Centric Networks Icn written by Nitul Dutta and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-29 with Computers categories.

This book aimed at bringing an insight to the ICN network, particularly various architectures, issues and challenges in the new networking paradigm. The book starts with an introduction to the new promising concept of ICN and its origin along with the reason behind this interesting innovation. Different architectures proposed so far in support of implementing the ICN is also discussed in details. Few of the challenges of ICN implementation are enlisted as caching, naming, routing, and security. Each of these challenges with recent development is covered in individual chapters. Moreover, integration of current trends in communication and computing like software defined networking and machine learning approach are another area that this book is focusing. All these chapters highlight the recent developments reported in the area and also discusses the future trends. The book provides an overview of the recent developments in future internet technologies, bringing together the advancements that have been made in ICN. The book includes three unique chapters in the field of ICN research. The first, is the SDN framework for implementing ICN by decoupling data and control plan. The machine learning models for predicting future trends in network traffic and other management activities is another important chapter. This chapter includes the possibilities of using machine learning models for trend prediction to help network administrators and service providers to take care of unexpected sudden change traffic pattern and user behaviour. The third most vital chapter is the security issues in ICN. This chapter includes various facts that influences the security of ICN. Issues involved in naming, caching and routing are discussed separately along with few recent works in these areas. Various types of attacks in ICN are also part of the discussion. The stated book would be useful for researchers in this area and will work as a reference for future work. Moreover, the content of the book would also be suitable as a supporting material for undergraduate and graduate level courses in computer science and electrical engineering.

Modeling And Optimization In Software Defined Networks

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Author : Konstantinos Poularakis
language : en
Publisher: Springer Nature
Release Date : 2022-06-01

Modeling And Optimization In Software Defined Networks written by Konstantinos Poularakis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.

This book provides a quick reference and insights into modeling and optimization of software-defined networks (SDNs). It covers various algorithms and approaches that have been developed for optimizations related to the control plane, the considerable research related to data plane optimization, and topics that have significant potential for research and advances to the state-of-the-art in SDN. Over the past ten years, network programmability has transitioned from research concepts to more mainstream technology through the advent of technologies amenable to programmability such as service chaining, virtual network functions, and programmability of the data plane. However, the rapid development in SDN technologies has been the key driver behind its evolution. The logically centralized abstraction of network states enabled by SDN facilitates programmability and use of sophisticated optimization and control algorithms for enhancing network performance, policy management, and security.Furthermore, the centralized aggregation of network telemetry facilitates use of data-driven machine learning-based methods. To fully unleash the power of this new SDN paradigm, though, various architectural design, deployment, and operations questions need to be addressed. Associated with these are various modeling, resource allocation, and optimization opportunities.The book covers these opportunities and associated challenges, which represent a ``call to arms'' for the SDN community to develop new modeling and optimization methods that will complement or improve on the current norms.

Modeling And Optimization In Software Defined Networks

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Author : Konstantinos Poularakis
language : en
Publisher:
Release Date :

Modeling And Optimization In Software Defined Networks written by Konstantinos Poularakis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with Software-defined networking (Computer network technology) categories.

Machine Learning For Networking

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Author : Éric Renault
language : en
Publisher: Springer
Release Date : 2019-05-10

Machine Learning For Networking written by Éric Renault and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-10 with Computers categories.

This book constitutes the thoroughly refereed proceedings of the First International Conference on Machine Learning for Networking, MLN 2018, held in Paris, France, in November 2018. The 22 revised full papers included in the volume were carefully reviewed and selected from 48 submissions. They present new trends in the following topics: Deep and reinforcement learning; Pattern recognition and classification for networks; Machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection; Optimization and new innovative machine learning methods; Performance analysis of machine learning algorithms; Experimental evaluations of machine learning; Data mining in heterogeneous networks; Distributed and decentralized machine learning algorithms; Intelligent cloud-support communications, resource allocation, energy-aware/green communications, software defined networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.

Artificial Intelligence For Autonomous Networks

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Author : Mazin Gilbert
language : en
Publisher: CRC Press
Release Date : 2018-09-25

Artificial Intelligence For Autonomous Networks written by Mazin Gilbert and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-25 with Computers categories.

Artificial Intelligence for Autonomous Networks introduces the autonomous network by juxtaposing two unique technologies and communities: Networking and AI. The book reviews the technologies behind AI and software-defined network/network function virtualization, highlighting the exciting opportunities to integrate those two worlds. Outlining the new frontiers for autonomous networks, this book highlights their impact and benefits to consumers and enterprise customers. It also explores the potential of the autonomous network for transforming network operation, cyber security, enterprise services, 5G and IoT, infrastructure monitoring and traffic optimization, and finally, customer experience and care. With contributions from leading experts, this book will provide an invaluable resource for network engineers, software engineers, artificial intelligence, and machine learning researchers.

Bringing Machine Learning To Software Defined Networks

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