[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117154-en":3,"doc-seo-117154-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},117154,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Machine Learning With Computer Networks - Techniques, Datasets, and Models - Primer Paper","Machine learning has broad applications in network environments, including solving optimization problems and improving network operations, while networks also enable machine learning training and inference in both centralized and distributed settings. The material provides a rigorous research orientation by consolidating core techniques, concrete frameworks, and relevant dataset access strategies. It further highlights training data as both benchmark and launch point for future investigation, serving as a practical entry for researchers working across machine learning and networking.","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","cbCaiu85ICCuS1oX","https://ap.wps.com/l/cbCaiu85ICCuS1oX","pdf",6697711,1,48,"English","en",105,"# Introduction\n# Machine Learning in Network Contexts\n## Applications and Network Roles\n# Datasets and Benchmarking\n# Techniques, Frameworks, and Models\n# Metrics, Tools, and Research Access","[{\"question\":\"How does machine learning relate to computer networks in this work?\",\"answer\":\"It explains that machine learning supports network-oriented optimization and operations, while networks in turn provide infrastructure for centralized or distributed training and inference.\"},{\"question\":\"Why are datasets important for research in machine learning for networks?\",\"answer\":\"Training data access enables benchmarking and acts as a springboard for further experiments and investigation, since model quality depends on data generalization.\"},{\"question\":\"What does the article aim to provide for researchers?\",\"answer\":\"It summarizes multiple techniques, frameworks, and models as a primer to help researchers start work on machine learning for networks or use networks for machine learning.\"}]","Machine Learning With Computer Networks - 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