An Efficient Request Assignment Method of Policy Enforcement Point Based on Markov Decision Process in Online Social Network

An Efficient Request Assignment Method of Policy Enforcement Point Based on Markov Decision Process in Online Social Network | 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 An Efficient Request Assignment Method of Policy Enforcement Point Based on Markov Decision Process in Online Social Network Fan Deng, Tao Liu, Yancong Wang, Boqi Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5414949/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract For artificial intelligence information systems, it is essential to make trade-offs for the requests that arrive in large quantities rapidly. To alleviate server congestion and enhance user experience, we construct an agent for the Policy Enforcement Point (PEP) to determine whether to disregard a request or assign it to a server for authentication. First, we formulate the request assignment problem as a Markov Decision Process (MDP) and employ the value iteration algorithm to obtain the optimal policy function, based on which the agent makes decisions. Second, we define the priority of requests according to the out-degree of the request sender and consider all the servers' loads and the current request priority as states. Third, the transition probability is calculated following the frequency of request arrivals and the time each server takes to process one request. Finally, comparative experiments on variable parameters are carried out, and the optimal parameters of our method are determined. With the optimal parameters, the performance of our method is compared with that of Myopic and Random Rejection (RR). The results show that our proposed method outperforms the other methods regarding the Service Request Metric (SRM) and retransmission times. Policy Enforcement Point Online Social Network Markov Decision Process Value Iteration Algorithm Artificial Intelligence Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5414949","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":389106281,"identity":"b29cc638-87b2-4159-aeea-08b05a97753c","order_by":0,"name":"Fan Deng","email":"","orcid":"","institution":"Xi'an University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Deng","suffix":""},{"id":389106282,"identity":"8e329429-be4c-4ddf-9cfd-902a5e65d018","order_by":1,"name":"Tao Liu","email":"data:image/png;base64,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","orcid":"","institution":"Xi'an University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Tao","middleName":"","lastName":"Liu","suffix":""},{"id":389106283,"identity":"562eebd6-783e-4bc0-a90e-8c2b15980813","order_by":2,"name":"Yancong Wang","email":"","orcid":"","institution":"Xidian University","correspondingAuthor":false,"prefix":"","firstName":"Yancong","middleName":"","lastName":"Wang","suffix":""},{"id":389106284,"identity":"2308b7b1-ef30-4f89-94b6-e882ad94beef","order_by":3,"name":"Boqi Zhang","email":"","orcid":"","institution":"Xidian University","correspondingAuthor":false,"prefix":"","firstName":"Boqi","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-11-08 08:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5414949/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5414949/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76940353,"identity":"dcea2c36-4576-449c-8f1e-b60657362c5c","added_by":"auto","created_at":"2025-02-22 20:53:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":897581,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5414949/v1_covered_484482e2-a91a-4d46-9f2a-934ac2d263d8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Efficient Request Assignment Method of Policy Enforcement Point Based on Markov Decision Process in Online Social Network","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Policy Enforcement Point, Online Social Network, Markov Decision Process, Value Iteration Algorithm, Artificial Intelligence","lastPublishedDoi":"10.21203/rs.3.rs-5414949/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5414949/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"For artificial intelligence information systems, it is essential to make trade-offs for the requests that arrive in large quantities rapidly. To alleviate server congestion and enhance user experience, we construct an agent for the Policy Enforcement Point (PEP) to determine whether to disregard a request or assign it to a server for authentication. First, we formulate the request assignment problem as a Markov Decision Process (MDP) and employ the value iteration algorithm to obtain the optimal policy function, based on which the agent makes decisions. Second, we define the priority of requests according to the out-degree of the request sender and consider all the servers' loads and the current request priority as states. Third, the transition probability is calculated following the frequency of request arrivals and the time each server takes to process one request. Finally, comparative experiments on variable parameters are carried out, and the optimal parameters of our method are determined. With the optimal parameters, the performance of our method is compared with that of Myopic and Random Rejection (RR). 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