[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118483-en":3,"doc-seo-118483-105":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},118483,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Control Traffic in SDN Systems by using Machine Learning techniques - Review","Rapid growth of Internet and mobile communication makes network infrastructures increasingly complex across devices, resources, and operational conditions, creating a need for smarter organization, management, maintenance, and optimization. Applying machine learning to network control is challenging in traditional distributed architectures, but SDN centralization, centralized visibility, software-based traffic monitoring, and dynamic forwarding-rule updates enable ML integration. This review surveys foundational SDN and ML literature and focuses on traffic classification, QoS prediction, routing optimization, and QoE-/security-oriented resource management, followed by challenges and broader perspectives.","Control Traffic inSDN Systems by using Machine Learning techniques: Review  \nShavan Askar1,2*, Diana Hussein2, Media Ibrahim1, Marwan Mohammed2  \n1Information System Engineering Department, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil, Iraq  \n2Department of Computer science, college of engineering, knowledge university, Erbil 44001, Iraq  \nEmail:*[shavan.askar@epu.edu.iq](shavan.askar@epu.edu.iq)  \nAbstract. Due to the rapid development of Internet and mobile communication technologies, which have spearheaded a fast growth of networking systems to become increasingly complex and diverse regarding infrastructure, devices, and resources. This requires further intelligence deployment to improve the organization, management, maintenance, and optimization of these networks. However, it is difficult to apply machine learning techniques in controlling and operating networks because of the inherent distributed structure of traditional networks. The centralized control of all network operations, holistic knowledge of the network, software-based monitoring of traffic, and updating of forwarding rules to enable the functions of (SDN) are factors that (SDN) has that facilitate the application of machine learning techniques. This study will make an extensive review of existing literature to be able to answer the research question of how machine learning techniques can be used in the context of the SDN. First, it gives a review of the foundational literature information. After this, a brief review of machine learning techniques is presented. We shall also delve into the application of machine learning techniques in the area of (SDN), with a sharp edge on traffic classification, prediction of Quality-of-Service (QoS), and optimization of routing and Quality-of-Experience (QoE) security management of the resource separately. Finally, we engage in discussions surrounding challenges and broader perspectives.  \nKeywords: Machine Learning (ML), Software-Defined Networking (SDN), Classifications of traffic, Management of resources.  \n1. Introduction  \nRecently, propelled by the rapid advancements in smart devices such as smartphones, smart cars, and smart home gadgets, coupled with the progress in network technologies like cloud computing and virtualization of the network, the volume of data traffic worldwide is experiencing exponential growth. To manage traffic allocation effectively and accommodate a significant the number of devices and networks are becoming increasingly diverse and complex. A typical network infrastructure comprises numerous devices, operates multiple  \nprotocols, and supports various applications. Wireless networks, for instance, employ a variety of the cells with distinct coverage of the transmission, level of the powers, and operational mechanisms. Furthermore, these networks utilize different technologies for the communication such as WiMAX, IEEE802.11 ac/ad, LTE and Bluetooth. The presence of diverse of the network infrastructure adds complexity to the networks, posing challenges in the efficiently organizing, managing, and maximizing of the network resources. One potential solution to address these challenges is to enhance the intelligence level in networks. In recent years, a Knowledge Plane (KP) approach has emerged asa promising avenue for tackling these issues[1] was introduced to enhance the Internet with automation, recommendation, and intelligence. This was accomplished through the implementation of the Machine Learning (ML) and cognitive methodologies. The central obstacle is in the intrinsic dispersion of the traditional network systems, for each node has localized insight and control over a rather minuscule part of the system. What we are looking at is executing control beyond the local domain while learning from nodes with only a partial view of the entire system[2]. Fortunately, recent advancements in (SDN) are poised to alleviate the challenges associated with knowledge acquisitio","cbCain2oHSTuTePk","https://ap.wps.com/l/cbCain2oHSTuTePk","pdf",989061,1,24,"English","en",105,"# Introduction\n## SDN and the need for intelligence\n## Why machine learning in SDN\n## Article organization\n# Related work and background\n## Foundational literature review\n## ML techniques overview\n# ML applications in SDN\n## Traffic classification\n## QoS prediction and resource management\n## Routing optimization and QoE/security\n# Challenges and broader perspectives","[{\"question\":\"Why is controlling traffic in traditional networks difficult for machine learning approaches?\",\"answer\":\"Traditional networks have an inherent distributed structure, where each node holds only localized insight and control, limiting holistic learning for network-wide decisions.\"},{\"question\":\"How does SDN enable the use of machine learning for traffic control?\",\"answer\":\"SDN separates the control plane from the data plane, allowing a centralized controller to maintain a holistic view and continuously collect real-time network state, packets, and flows for ML-driven decisions.\"},{\"question\":\"Which SDN traffic and performance problems does the review focus on?\",\"answer\":\"The review emphasizes traffic classification, QoS prediction, routing optimization, and resource management related to QoE and security, along with challenges and broader perspectives.\"}]","Control Traffic in SDN Systems by using Machine Learning techniques - Review | PDF",1785683824,60,{"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},"control-traffic-in-sdn-systems-by-using-machine-learning-techniques-review","",{"@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/control-traffic-in-sdn-systems-by-using-machine-learning-techniques-review/118483/",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","2026-08-02",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},"Why is controlling traffic in traditional networks difficult for machine learning approaches?","Question",{"text":75,"@type":76},"Traditional networks have an inherent distributed structure, where each node holds only localized insight and control, limiting holistic learning for network-wide decisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does SDN enable the use of machine learning for traffic control?",{"text":80,"@type":76},"SDN separates the control plane from the data plane, allowing a centralized controller to maintain a holistic view and continuously collect real-time network state, packets, and flows for ML-driven decisions.",{"name":82,"@type":73,"acceptedAnswer":83},"Which SDN traffic and performance problems does the review focus on?",{"text":84,"@type":76},"The review emphasizes traffic classification, QoS prediction, routing optimization, and resource management related to QoE and security, along with challenges and broader perspectives.","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]