[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126315-en":3,"doc-seo-126315-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126315,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",8,"Research & Report","Machine Learning Applications for Event Routing in Streaming Systems - Research overview","The paper reviews machine learning methods for optimizing event routing in distributed streaming architectures, aiming to classify existing approaches and evaluate their potential across real operational scenarios. It systematically analyzes methods ranging from classical reinforcement learning to modern deep neural networks, highlighting key limitations and trade-offs. Three algorithm classes are examined—reinforcement learning, deep networks, and ensemble/evolutionary techniques—with focus on response time, scalability, and dynamic adaptability. Hybrid strategies that combine models are emphasized for higher reliability and recommendation accuracy.","Machine Learning Applications for Event Routing in  \nStreaming Systems  \nVladyslav Vodopianov *  \nSenior Software Engineer at Wirex,Kyiv, Ukraine  \n[Email:vladyslav.vodopianov@gmail.com](Email:vladyslav.vodopianov@gmail.com)  \nAbstract  \nThe paper provides a broad overview and classification of machine learning methods used to optimize routing in distributed streaming architectures. The aim of the study is to provide a detailed analysis of existing approaches: from classical reinforcement learning algorithms to modern deep neural networks, with an assessment of their potential in various operational scenarios and identification of key limitations. The methodological basis was a systematic review of publications dealing with intelligent routing, real-time data processing, and integration of ML solutions into system pipelines. Three main classes of algorithms were identified and considered: reinforcement learning methods (including DQN and actor-critic), deep networks (CNN, RNN and their hybrids), as well as ensemble and evolutionary techniques. The advantages and disadvantages of each class are analyzed in terms of key criteria—response time to flow changes, scalability in the number of nodes, and the ability to dynamically adapt. Special attention was paid to hybrid strategies that combine several models to increase the reliability and accuracy of recommendations on event transmission routes. In conclusion, the main conclusions about the current state of research are formulated and promising areas are outlined: the development of more robust architectures with explicable decision-making logic, as well as the integration of graph neural networks for modeling complex topologies of distributed systems. The presented results will be useful for engineers developing streaming platforms, big data analysis specialists, and research groups working on information channel optimization tasks.  \nKeywords: event routing; streaming systems; machine learning; reinforcement learning; deep learning; adaptive routing; streaming data processing; intelligent systems; performance optimization; traffic management.  \nReceived: 6/21/2025  \nAccepted: 8/21/2025  \nPublished: 8/31/2025  \n* Corresponding author.  \n1. Introduction  \nStreaming platforms designed to continuously process event data are becoming important for areas such as the Internet of Things, algorithmic trading, and social media monitoring. The efficiency of such systems is measured by the speed and accuracy of delivery of each incoming event to the corresponding computing component or service [1] . Classical routing methods based on given rules or elementary heuristics are often unable to respond to rapidly changing loads and network conditions: this leads to local overloads, increased response times, and deterioration in overall system throughput. In this context, the use of machine learning methods for routing tasks opens up broad prospects: adaptive, self-learning algorithms can not only predict optimal routes for events based on their features, but also take into account the current state of nodes and communication channels. At the sametime, the scientific community still lacks a unified methodology for systematically evaluating and comparatively analyzing various ML approaches in relation to heterogeneous and highly loaded streaming environments. Complex models that combine predictive analytics of event characteristics with monitoring of infrastructure resources are not sufficiently developed.  \nThe aim of the study is to conduct a broad review of modern methods of using machine learning for routing in streaming systems, identify and classify their strengths and weaknesses, and determine the most promising areas for development.  \nThe scientific novelty lies in the formulation of a conceptual basis for the selection and integration of ML routing algorithms focused on the balance between processing speed, delivery accuracy and adaptability to load dynamicsand data structure.  \nThe aut","cbCaioC46IIzFkVN","https://ap.wps.com/l/cbCaioC46IIzFkVN","pdf",649670,5,1,10,"English","en",105,"# Introduction\n## Motivation and research gap\n## Study aim and novelty\n## Hypothesis and limitations\n# Materials and methods\n## Deep reinforcement learning in SDN\n## Supervised learning for QoS-driven routing\n# Algorithm classes and hybrid strategies\n## Reinforcement learning methods\n## Deep networks\n## Ensemble and evolutionary techniques\n## Hybrid model integration\n# Challenges and future directions\n## Explainable decision logic\n## Graph neural networks for topology modeling","[{\"question\":\"Why are classical routing methods insufficient for streaming systems?\",\"answer\":\"Classical rule-based or heuristic routing cannot quickly react to rapidly changing load and network conditions, causing local overloads, higher response times, and reduced overall throughput.\"},{\"question\":\"What main algorithm classes are identified for event routing optimization?\",\"answer\":\"The paper groups approaches into reinforcement learning methods (e.g., DQN and actor-critic), deep networks (CNN/RNN and hybrids), and ensemble/evolutionary techniques.\"},{\"question\":\"How do hybrid strategies improve routing recommendations?\",\"answer\":\"Hybrid strategies combine multiple models to enhance reliability and accuracy of recommended event transmission routes, balancing different sources of prediction and decision capability.\"}]","Machine Learning Applications for Event Routing in Streaming Systems - Research overview | PDF",1785904414,25,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-applications-for-event-routing-in-streaming-systems-research-overview","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-applications-for-event-routing-in-streaming-systems-research-overview/126315/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why are classical routing methods insufficient for streaming systems?","Question",{"text":77,"@type":78},"Classical rule-based or heuristic routing cannot quickly react to rapidly changing load and network conditions, causing local overloads, higher response times, and reduced overall throughput.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What main algorithm classes are identified for event routing optimization?",{"text":82,"@type":78},"The paper groups approaches into reinforcement learning methods (e.g., DQN and actor-critic), deep networks (CNN/RNN and hybrids), and ensemble/evolutionary techniques.",{"name":84,"@type":75,"acceptedAnswer":85},"How do hybrid strategies improve routing recommendations?",{"text":86,"@type":78},"Hybrid strategies combine multiple models to enhance reliability and accuracy of recommended event transmission routes, balancing different sources of prediction and decision capability.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,129,132,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":22,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":20,"slug":138},19,"General","general"]