[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117216-en":3,"doc-seo-117216-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117216,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",6,"Technology","Machine Learning With Computer Networks - Techniques, Datasets, and Models","Machine learning has broad applications in network environments, including optimization and network operations management. Networks also play a key role in enabling machine learning training and inference, whether in centralized or distributed settings. Rigorous research requires mastery of core techniques, suitable frameworks, and access to relevant datasets, while training data availability supports benchmarking and further experimentation. This article consolidates techniques as an introductory primer for researchers working with learning for networks or network-enabled learning.","Machine Learning With Computer Networks: Techniques, Datasets, and Models  \nHAITHAM AFIFI1,(Member, IEEE), SABRINA POCHABA2, ANDREAS BOLTRES3, DOMINIC LANIEWSKI4,(Graduate Student Member, IEEE), JANEK HABERER5, LEONARD PAELEKE6,7, REZA POORZARE8,(Member, IEEE), DANIEL STOLPMANN9,  \nNIKOLAS WEHNER10, ADRIAN REDDER11, ERIC SAMIKWA12, AND MICHAEL SEUFERT13,(Senior Member, IEEE)  \n1Accenture, 61476 Kronberg im Taunus, Germany  \n2 Salzburg Research Forschungsgesellschaft m.b.H., 5020 Salzburg, Austria  \n3Autonomous Learning Robots Laboratory, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany  \n4Institute of Computer Science, Osnabrück University, 49076 Osnabrück, Germany  \n5Distributed Systems Group, Kiel University, 24118 Kiel, Germany  \n6Digital Engineering Faculty, University of Potsdam, 14482 Potsdam, Germany  \n7Digital Health & Machine Learning, Hasso Plattner Institute, 14482 Potsdam, Germany  \n8Wirtschaft Center of Applied Research, Data-Centric Software Systems (DSS) Research Group, Institute of Applied Research, Hochschule Karlsruhe Technik, 76133 Karlsruhe, Germany  \n9Institute of Communication Networks, Hamburg University of Technology, 21073 Hamburg, Germany  \n10Chair of Communication Networks, University ofWürzburg, 97074 Würzburg, Germany  \n11Universität Paderborn, 33098 Paderborn, Germany  \n12Institute of Computer Science, University of Bern, 3012 Bern, Switzerland  \n13Chair of Networked Embedded Systems and Communication Systems, University of Augsburg, 86159 Augsburg, Germany Corresponding author: Michael Seufert ([michael.seufert@uni-a.de](michael.seufert@uni-a.de))  \nThis work was supported by German Research Foundation [Deutsche Forschungsgemeinschaft (DFG)] under Grant SE 3163/3-1, project number: 500105691 (UserNet). This work was also supported by the Federal Ministry of Education and Research of Germany under Grant 16KISK011 (Open6GHub) as well as by the Federal Ministry for Economic Affairs and Climate Action of Germany under Grant 68GX21002 (Marispace-X) .  \nABSTRACT Machine learning has found many applications in network contexts. These include solving optimisation problems and managing network operations. Conversely, networks are essential for facilitating machine learning training and inference, whether performed centrally or in a distributed fashion. To conduct rigorous research in this area, researchers must have a comprehensive understanding of fundamental techniques, specific frameworks, and access to relevant datasets. Additionally, access to training data can serve as a benchmark or a springboard for further investigation. All these techniques are summarized in this article; serving as a primer paper and hopefully providing an efficient start for anybody doing research regarding machine learning for networks or using networks for machine learning.  \nINDEX TERMS Computer networking, datasets, machine learning, metrics, tools.  \nI. INTRODUCTION  \nIn recent years, the ever-growing interconnection of businesses and people and their increased reliance on networked services has prompted computer network architectures to continually grow in size and complexity. Moreover, with the increased efficiency and convenience of network-based services and businesses, the expectations of enterprisesand people with respect to network performance indicators such as latency, throughput, reliability and resilience are  \nThe associate editor coordinating the review of this manuscript and  \napproving it for publication was Kaigui Bian  .  \nsteadily growing. Consequently, conventional algorithmic and heuristic-based approaches for network management tasks are starting to fall behind the expected levels of performance, as they fail to deliver timely and nuanced decisions in the face of the complex environment they are operating in. Meanwhile, Machine Learning (ML) has shown remarkable results in various problem domains such as discovering new antibiotic drugs [1], generating high-fidelity images from arbitr","cbCaitE1d766tyG2","https://ap.wps.com/l/cbCaitE1d766tyG2","pdf",6495054,1,48,"English","en",105,"# Introduction\n## Motivation and Context\n## Why ML for Networks\n## Key Concepts: AI and Machine Learning","[{\"question\":\"How do machine learning methods support network-related tasks?\",\"answer\":\"They can address optimization problems and help manage network operations by providing data-driven decisions in complex environments.\"},{\"question\":\"Why are computer networks important for machine learning?\",\"answer\":\"Networks facilitate training and inference, supporting centralized and distributed execution of machine learning workflows.\"},{\"question\":\"What do researchers need to study machine learning for networks effectively?\",\"answer\":\"A solid understanding of fundamental techniques and frameworks, plus access to relevant datasets, so that training data can serve as both a benchmark and a starting point for new investigations.\"}]","Machine Learning With Computer Networks - 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