Performance-aware Optimal Controller Placements Via Genetic Algorithms for Software Defined Networking

Performance-aware Optimal Controller Placements Via Genetic Algorithms for Software Defined Networking | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Performance-aware Optimal Controller Placements Via Genetic Algorithms for Software Defined Networking Gutema Bote Nuguse, Ketema Adere Gemeda, Perumalla Janaki Ramulu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4548625/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Software-defined Network is a new paradigm of providing the efficient network management using the concept of control and data place separation. Multi-controllers designing is a promising way to achieve reliability and scalability. However, it brings the new problem of controller placement in a distributed architecture. For this, two recent approaches of controller placement (CP) are based on controller placement simulated annealing (CPSA) and controller placement particle swarm optimization (CPPSO). However, these approaches are still not effective in placement of controllers. Thus, there is performance degrading of the systems. To solve these problems, the controller placement based on a Genetic Algorithm (CPGA) has been proposed in this research. The proposed CPGA has used the fitness value of each node to locate the controllers at their optimal place. Also, the GA operations continues until it gets the optimal placement of controllers and after locating the controller at their appropriate place, it was used for a long time in the case of near optimal rather than the existing approaches. The performance comparison has been done based on parameters such as throughput and delay. It is observed with comparison of CPSA and CPPSO that the proposed CPGA outperforms on given parameters. The proposed CPGA shows efficiency in placing controllers at their optimal locations. Software Defined Networking Genetic Algorithms Controller Placement Problem Throughput Delay Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4548625","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322622128,"identity":"6f549487-496b-400d-85e5-bde40f5ac6b8","order_by":0,"name":"Gutema Bote Nuguse","email":"","orcid":"","institution":"Adama University: Adama Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Gutema","middleName":"Bote","lastName":"Nuguse","suffix":""},{"id":322622129,"identity":"448278ab-c8e3-4f52-ac01-e29f4a08370b","order_by":1,"name":"Ketema Adere Gemeda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYBACCTjrABB/wBDFqoUZoYVxBlS1BD5tKFqYeYjRItnef/DRjRoGeb7jvQ8f29TU1RkcYD54m4fBpg6XFmmew8zGOccYDGeeOW4MZByWMDjAlmzNw5CG0xY5iWQ26Rw2BsYNN9JAjANALTxm0jwMh3FrkX/M/jvnH4P9hvvP2H9b/KsDauH/BtTyH6cWaQlmNubcNobEDTfY2JgZ25hBtrABtRzA7f2eZGPp3D6J5Jln0pgle/sOS848zGZsOccgWbIBhxaJ4wcffs75ZmPbd/wY44cf3+r4+Y43P7zxpsKOH5ctMJ1IbHBEGRDQMApGwSgYBaMALwAAd65Nbo9VucAAAAAASUVORK5CYII=","orcid":"","institution":"Adama University: Adama Science and Technology University","correspondingAuthor":true,"prefix":"","firstName":"Ketema","middleName":"Adere","lastName":"Gemeda","suffix":""},{"id":322622130,"identity":"8a854473-2d1e-41b0-b5f3-6e9c2a63b59d","order_by":2,"name":"Perumalla Janaki Ramulu","email":"","orcid":"https://orcid.org/0000-0002-7856-8638","institution":"Adama University: Adama Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Perumalla","middleName":"Janaki","lastName":"Ramulu","suffix":""},{"id":322622131,"identity":"9f2efaff-d354-4f83-a7ee-09bc1cacbe95","order_by":3,"name":"T. 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