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 QoS Aware Deep Reinforcement Learning Based Intelligent Routing for Software Defined Networks Supporting Multi Applications written by Chih-Ling Liu and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book QoS Aware Virtual Network Embedding written by Chunxiao Jiang and published by Springer Nature. This book was released on 2022-01-01 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: As an important future network architecture, virtual network architecture has received extensive attention. Virtual network embedding (VNE) is one of the core services of network virtualization (NV). It provides solutions for various network applications from the perspective of virtual network resource allocation. The Internet aims to provide global users with comprehensive coverage. The network function requests of hundreds of millions of end users have brought great pressure to the underlying network architecture. VNE algorithm can provide effective support for the reasonable and efficient allocation of network resources, so as to alleviate the pressure off the Internet. At present, a distinctive feature of the Internet environment is that the quality of service (QoS) requirements of users are differentiated. Different regions, different times, and different users have different network function requirements. Therefore, network resources need to be reasonably allocated according to users' QoS requirements to avoid the waste of network resources. In this book, based on the analysis of the principle of VNE algorithm, we provide a VNE scheme for users with differentiated QoS requirements. We summarize the common user requirements into four categories: security awareness, service awareness, energy awareness, and load balance, and then introduce the specific implementation methods of various differentiated QoS algorithms. This book provides a variety of VNE solutions, including VNE algorithms for single physical domain, VNE algorithms for across multiple physical domains, VNE algorithms based on heuristic method, and VNE algorithms based on machine learning method.

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 Virtualization enabled Adaptive Routing for QoS aware Software Defined Networks written by Alba Xifra Porxas and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: [ANGLÈS] Software-Defined Networking (SDN) has been recognized as the next-generation networking paradigm. It is a fast-evolving technology that decouples the network data plane, which are the network devices that forward traffic, from the network control plane, which is the software logic that controls ultimately how traffic is forwarded through the network. A logically centralized controller is responsible for all the control decisions and communication among the forwarding elements. It allows to control the network behavior from a single high level control program. However, Software-Defined Networks use many of its network resources inefficiently, which leads to over-loading network links, congestion in the queues and end-to-end packet delays. Consequently, it becomes clear that routing decisions affect the overall performance of a communication's network. Performance is determined in terms of Quality of Service guarantees, i.e. throughput, average packet delay, jitter and losses. Thus, a QoS-aware routing algorithm is required. Current traffic engineering techniques and state-of-the-art routing algorithms do not effectively use the merits of SDNs, such as global centralized visibility, real-time fast decisions, control and data plane decoupling, network management simplification and portability. In this thesis, we developed two new QoS-aware routing algorithms that exploit the advantages that SDN brings to improve the network performance. Two different scenarios have been studied: a centralized and a distributed models. The centralized scenario simplifies the management of complex flows and the customization, but scalability issues arise. In contrast, the distributed scenario is more scalable, but there may be state inconsistency and increase of shared information. In general, a centralized approach is better for data centers or home networks, whereas a distributed approach is better for large scale networks, e.g. cloud environments. First, the centralized SDN controller model is discussed, for which a multi-tenancy management framework is proposed to fulfill the quality-of-services (QoSs) requirements through tenant isolation, prioritization and flow allocation. A network virtualization algorithm is provided to isolate and prioritize tenants from different clients. Furthermore, a novel routing scheme, called QoS-aware Virtualization-enabled Routing (QVR), is presented. It combines the proposed virtualization technique and a QoS-aware framework to enable flow allocation with respect to different tenant applications. Simulation results confirm that the proposed QVR algorithm surpasses the conventional algorithms with less traffic congestion and packet delay. This facilitates reliable and efficient data transportation in generalized SDNs. Therefore, it yields to service performance improvement for numerous applications and enhancement of client isolation. Second, a distributed SDN controller model is analyzed. The network is divided into different clusters, and hierarchically split in two levels. This architecture leads to smaller sizes of routing tables in the switches, and substantially lesser calculations and updates of routing tables from the controller. Moreover, a new algorithm is developed, called QoS-aware Reinforcement Learning Routing (QRLR), where reinforcement learning is applied to the routing problem. The modeling of the reward function calculation solution allows the customization of the different requirements for each type of traffic, thus providing flexibility and adaptability to different flows and its requirements.

Download or read book Mobile Web and Intelligent Information Systems written by Muhammad Younas and published by Springer Nature. This book was released on with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Convergence of Deep Learning and Internet of Things Computing and Technology written by Kavitha, T. and published by IGI Global. This book was released on 2022-12-19 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital technology has enabled a number of internet-enabled devices that generate huge volumes of data from different systems. This large amount of heterogeneous data requires efficient data collection, processing, and analytical methods. Deep Learning is one of the latest efficient and feasible solutions that enable smart devices to function independently with a decision-making support system. Convergence of Deep Learning and Internet of Things: Computing and Technology contributes to technology and methodology perspectives in the incorporation of deep learning approaches in solving a wide range of issues in the IoT domain to identify, optimize, predict, forecast, and control emerging IoT systems. Covering topics such as data quality, edge computing, and attach detection and prediction, this premier reference source is a comprehensive resource for electricians, communications specialists, mechanical engineers, civil engineers, computer scientists, students and educators of higher education, librarians, researchers, and academicians.

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 Security Issues in Fog Computing from 5G to 6G written by Chintan Bhatt and published by Springer Nature. This book was released on 2022-09-08 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides an examination of how fog security is changing the information technology industry and will continue to in the next decade. The authors first discuss how fog enables key applications in wireless 5G, the Internet of Things, and big data. The book then presents an overview of fog/edge computing, focusing on its relationship with cloud technology, Internet of Things and the future with the use of secure 5G/6G communication. The book also presents a comprehensive overview of liabilities in fog/edge computing within multi-level architectures and the intelligent management. The last part of the book reviews applications of fog/edge computing in smart cities, including in Industrial IoT, edge-based augmented reality, data streaming, and blockchain-based.

Download or read book Towards new e Infrastructure and e Services for Developing Countries written by Rafik Zitouni and published by Springer Nature. This book was released on 2021-03-03 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed proceedings of the 12th International Conference on e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2020, held in Ebène City, Mauritius, in December 2020. Due to COVID-19 pandemic the conference was held virtually. The 20 full papers were carefully selected from 90 submissions. The papers are organized in four thematic sections on dynamic spectrum access and mesh networks; wireless sensing and 5G networks; software-defined networking; Internet of Things; e-services and big data; DNS resilience and performance.

Download or read book A QoS aware Framework for Traffic Classificationin Software defined Networks written by Josep Xavier Salvat Lozano and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: [ANGLÈS] How can we build a system for traffic classification so that different QoS levels can be identified? How can we analyse different network parameters and identify a specific QoS for every flow? The purp ose of this work is to build a system capable of classifying traffic according to flows' different QoS requirements. Next, classification results are going to b e used to improve the state-of-the-art traffic engineering techniques in SDN networks. As in SDN networks ele- ments are programmable from the controller, different QoS paths can b e implemented so that different traffic flows can see diferent QoS. This traffic classification system go es b eyond the classical state-of-the-art classification into mice/elephant flows. We want to identify different priority classes so that higher priority classes are able to access to more network resources. We seek improving the overall network p erformance. The approach for tackling this problem would be somehow equal to the approach used for building a new machine learning system. We are going to define a set of variables, measure them, and classify traffic according to the values of that variables into different classes. We will start defining a measurement layer for SDN. To classify trafic we first need to observe what prop erties is exhibiting a traffic flow. This first layer will enables us to measure different flow prop erties as well as p erforming other management tasks. We are going to define a layer so that we could gather flow information with minimal network over-head. This system will b e implemented distributely in the switches and will leverage flow tables switches to improve its functionalities. Second we are going to use the information captured to calculate various flow statistical fingerprints so that we can infer their network resources requirements. We will target only QoS signifficant flows. We are going to develop a machine learning algorithm in the scop e of semi-sup ervised learning that would learn form lab elled and unlab elled data to infer the most likely QoS. In particular, we are going to apply a machine learning algorithm known as Laplacian SVM to classify various network traffic flows into different QoS classes. Finally, we will prototyp e this algorithm and try it in a real world data set.

Download or read book Proceedings of International Conference on Deep Learning Computing and Intelligence written by Gunasekaran Manogaran and published by Springer Nature. This book was released on 2022-04-26 with total page 698 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers selected papers presented at the International Conference on Deep Learning, Computing and Intelligence (ICDCI 2021), organized by Department of Information Technology, SRM Institute of Science and Technology, Chennai, India, during January 7–8, 2021. The conference is sponsored by Scheme for Promotion of Academic and Research Collaboration (SPARC) in association with University of California, UC Davis and SRM Institute of Science and Technology. The book presents original research in the field of deep learning algorithms and medical imaging systems, focusing to address issues and developments in recent approaches, algorithms, mechanisms, and developments in medical imaging.

Download or read book A QoS aware Routing Algorithm for SDN based Data Center Networks written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book 6G Enabled Fog Computing in IoT written by Mohit Kumar and published by Springer Nature. This book was released on 2023-10-21 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past few years, the demand for data traffic has experienced explosive growth thanks to the increasing need to stay online. New applications of communications, such as wearable devices, autonomous systems, drones, and the Internet of Things (IoT), continue to emerge and generate even more data traffic with vastly different performance requirements. With the COVID-19 pandemic, the need to stay online has become even more crucial, as most of the fields, would they be industrial, educational, economic, or service-oriented, had to go online as best as they can. As the data traffic is expected to continuously strain the capacity of future communication networks, these networks need to evolve consistently in order to keep up with the growth of data traffic. Thus, more intelligent processing, operation, and optimization will be needed for tomorrow’s communication networks. The Sixth Generation (6G) technology is latest approach for mobile systems or edge devices in terms of reduce traffic congestions, energy consumption blending with IoT devices applications. The 6G network works beyond the 5G (B5G), where we can use various platforms as an application e.g. fog computing enabled IoT networks, Intelligent techniques for SDN network, 6G enabled healthcare industry, energy aware location management. Still this technology must resolve few challenges like security, IoT enabled trust network. This book will focus on the use of AI/ML-based techniques to solve issues related to 6G enabled networks, their layers, as well as their applications. It will be a collection of original contributions regarding state-of-the-art AI/ML-based solutions for signal detection, channel modeling, resource optimization, routing protocol design, transport layer optimization, user/application behavior prediction 6G enabled software-defined networking, congestion control, communication network optimization, security, and anomaly detection. The proposed edited book emphasis on the 6G network blended with Fog-IoT networks to introduce its applications and future perspectives that helps the researcher to apply this technique in their domain and it may also helpful to resolve the challenges and future opportunities with 6G networks.

Download or read book Artificial Intelligence and Sustainable Computing written by Manjaree Pandit and published by Springer Nature. This book was released on with total page 714 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Cloud Based Big Data Analytics in Vehicular Ad Hoc Networks written by Rao, Ram Shringar and published by IGI Global. This book was released on 2020-09-11 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vehicular traffic congestion and accidents remain universal issues in today’s world. Due to the continued growth in the use of vehicles, optimizing traffic management operations is an immense challenge. To reduce the number of traffic accidents, improve the performance of transportation systems, enhance road safety, and protect the environment, vehicular ad-hoc networks have been introduced. Current developments in wireless communication, computing paradigms, big data, and cloud computing enable the enhancement of these networks, equipped with wireless communication capabilities and high-performance processing tools. Cloud-Based Big Data Analytics in Vehicular Ad-Hoc Networks is a pivotal reference source that provides vital research on cloud and data analytic applications in intelligent transportation systems. While highlighting topics such as location routing, accident detection, and data warehousing, this publication addresses future challenges in vehicular ad-hoc networks and presents viable solutions. This book is ideally designed for researchers, computer scientists, engineers, automobile industry professionals, IT practitioners, academicians, and students seeking current research on cloud computing models in vehicular networks.