[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119813-en":3,"doc-seo-119813-105":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},119813,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Scheduling in TSN networks using machine learning - Degree project","Mass Ethernet adoption across multiple sectors creates the need for deterministic solutions that guarantee Quality of Service (QoS) for time-triggered flows. This project studies scheduling feasibility for three classes of time-triggered flows with different timing constraints on a simple TSN topology with two TSN nodes connected by a single link. Supervised machine learning is used to predict feasibility using K-NN and SVM, trained via Leave One Out Cross-Validation, and supported by a hybrid verification strategy to reduce dataset label generation effort.","FINAL DEGREE PROJECT  \nTITLE: Scheduling in TSN Networks using machine learning DEGREE: Bachelor’s degree in Network Engineering  \nAUTHOR: Arnau Martínez Lopera  \nDIRECTOR: Anna Agustí  \nDATA: July 7, 2023  \nTitle: Scheduling in TSN networks using machine learning  \nAuthor: Arnau Martínez Lopera  \nDirector: Anna Agustí  \nDate: July 7, 2023  \nOverview  \nThe massive adoption of Ethernet technology in multiple sectors, produces the need to provide deterministic solutions to ensure a Quality of Service (QoS) that meets the requirements of time-triggered flows. For this, the Time-Sensitive Networking (TSN) Task Group (TG) of the IEEE 802.1 developed a set of standards that define mechanisms for time-sensitive transmissions of data over Ethernet networks.  \nThis project focuses on studying the feasibility of scheduling three classes of time-triggered flows with different time constraints over a simple network topology, which is made from two TSN (Time-Sensitive Networking) nodes connected through a link. Scheduling multiple time-triggered flows is a complex problem because the scheduling, if exists, must meet the time constraints of all these flows.  \nTo address this challenge, we explore the potential of using supervised machine learning classification models to accurately predict the feasibility of scheduling a given set of time-triggered flows, meeting their time-constraints, in a TimeSensitive Network (TSN) .  \nSupervised models require a training dataset that contains a data matrix and a class label vector. To obtain the class label vector of each observation, we use an adaptation of the implementation developed in [27] of the Integer Linear Programming (ILP) model introduced in [33] .  \nTwo different models are considered: K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM) . These algorithms are tested and built from the application of the Leave One Out Cross-Validation (LOOCV) technique with the generated datasets, and the results obtained are compared and discussed.  \nFinally, a hybrid verification strategy is proposed to train and test machine learning models, drastically reducing the resources and computation time originally required to compute the class label of each observation of the dataset.  \nTítulo: Scheduling in TSN networks using machine learning  \nAutor: Arnau Martínez Lopera  \nDirectora: Anna Agustí  \nFecha: 7 de julio de 2023  \nResumen  \nLa adopción masiva de la tecnología Ethernet en múltiples sectores, produce lanecesidad de brindar soluciones deterministas para asegurar una Calidad de Servicio (QoS) que cumpla con los requerimientos de los flujos sensibles al time (en adelante TT por las siglas en inglés Time-Triggered) . Para ello, el grupo de trabajo TimeSensitive Networking (TSN) del IEEE 802.1 desarrolló un conjunto de estándares que definen mecanismos para transmitir flujos de datos con requisitos temporales estrictosa través de redes Ethernet.  \nEste proyecto se enfoca en estudiar la viabilidad de programar varios flujos de tresclases TT diferentes sobre una topología de red simple, compuesta de dos nodos TSN (Time-Sensitive Networking) conectados a través de un enlace. Programar la transmisión de múltiples flujos TT es un problema complejo ya que la solución, si existe, debe garantizar que se cumplen todos los requisitos temporales de todos los flujos atransmitir.  \nPara abordar este desafío, en este trabajo exploramos el potencial del uso de modelos de clasificación de aprendizaje supervisado para predecir con precisión, la viabilidadde programar un conjunto dado de flujos TT, cumpliendo con sus restricciones de tiempo, en una red (TSN) .  \nLos modelos supervisados requieren un conjunto de datos de entrenamiento formadospor una matriz de datos y un vector de etiquetas de clase. Para generar las etiquetas de clase, en este trabajo utilizamos una adaptación de la implementación desarrollada en [27] del modelo ILP definido en [33] .  \nEn este proyecto se consideran dos modelos diferentes: K-Nea","cbCairLMvEc8LN4B","https://ap.wps.com/l/cbCairLMvEc8LN4B","pdf",1925445,1,57,"English","en",105,"# Introduction\n## Background and problem statement\n# CHAPTER 1. Introducing Time Sensitive Networks (TSN)\n## IEEE802.1Qav (Credit Based Shaper)\n## IEEE802.1Qbv (Time Aware Shaper)\n## Complexity Problems\n## Applying TSN\n# CHAPTER 2. Dataset Generation\n## Integer Linear Programming (ILP)\n## Implementation\n## Enhancing the ILP implementation\n## Executing the ILP","[{\"question\":\"What problem does the project address in TSN networks?\",\"answer\":\"It addresses deterministic scheduling for time-triggered flows so Quality of Service requirements are met under strict time constraints.\"},{\"question\":\"How do the machine learning models predict scheduling feasibility?\",\"answer\":\"They use supervised classification (K-NN and SVM) trained on datasets where feasibility labels are derived from an Integer Linear Programming approach.\"},{\"question\":\"What purpose does the hybrid verification strategy serve?\",\"answer\":\"It reduces the resources and computation time needed to generate class labels for each observation in the dataset.\"}]","Scheduling in TSN networks using machine learning - Degree project | PDF",1785726441,144,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"scheduling-in-tsn-networks-using-machine-learning-degree-project","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/scheduling-in-tsn-networks-using-machine-learning-degree-project/119813/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the project address in TSN networks?","Question",{"text":75,"@type":76},"It addresses deterministic scheduling for time-triggered flows so Quality of Service requirements are met under strict time constraints.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the machine learning models predict scheduling feasibility?",{"text":80,"@type":76},"They use supervised classification (K-NN and SVM) trained on datasets where feasibility labels are derived from an Integer Linear Programming approach.",{"name":82,"@type":73,"acceptedAnswer":83},"What purpose does the hybrid verification strategy serve?",{"text":84,"@type":76},"It reduces the resources and computation time needed to generate class labels for each observation in the dataset.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]