Enabling Knowledge Defined Networks Deep Reinforcement Learning Graph Neural Networks And Network Analytics
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Enabling Knowledge-defined Networks : Deep Reinforcement Learning, Graph Neural Networks and Network Analytics
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.
Applied Graph Data Science
Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques. - Provides comprehensive coverage of the emerging paradigm of graph data science and its real-world applications - Gives readers practical guidance on how to approach and solve complex data analysis problems using graph data science, with an emphasis on deep analysis techniques including graph neural networks (GNNs), machine learning, algorithms, graph databases, and graph query languages - Covers extended graph models such as bipartite directed graphs of place-transition nets, graphs with dynamical processes defined on them - Petri and Sleptsov nets, and graphs as programming languages - Presents all the key tools and techniques as well as the foundations of graph theory, including mathematical concepts, research, and graph analytics
5G and Beyond Wireless Systems
This book presents the fundamental concepts, recent advancements, and opportunities for future research in various key enabling technologies in next-generation wireless communications. The book serves as a comprehensive source of information in all areas of wireless communications with a particular emphasis on physical (PHY) layer techniques related to 5G wireless systems and beyond. In particular, this book focuses on different emerging techniques that can be adopted in 5G wireless networks. Some of those techniques include massive-MIMO, mm-Wave communications, spectrum sharing, device-to-device (D2D) and vehicular to anything (V2X) communications, radio-frequency (RF) based energy harvesting, and NOMA. Subsequent chapters cover the fundamentals and PHY layer design aspects of different techniques that can be useful for the readers to get familiar with the emerging technologies and their applications.