A joint task caching and computation offloading scheme based on Deep Reinforcement Learning

A joint task caching and computation offloading scheme based on Deep Reinforcement Learning | 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 A joint task caching and computation offloading scheme based on Deep Reinforcement Learning Huizi Tian, Lin Zhu, Long Tan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3972282/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2024 Read the published version in Peer-to-Peer Networking and Applications → Version 1 posted 9 You are reading this latest preprint version Abstract Considering the dynamic variability of the vehicular edge environment and the limited edge servers resources, this paper proposes a joint task caching and computation offloading scheme based on deep reinforcement learning (DRL). Considering that the motion trajectories of different vehicles overlap and their task requests may be the same, this paper designs a vehicle-edge-cloud computing framework to fully use the cache resources of vehicles, edge servers, and clouds to reduce task processing delays and energy consumption. Secondly, this paper adopts a method of partial offloading and collaboration between edge servers, which fully utilizes the computing resources of vehicles, edge servers, and the cloud, reducing the burden of vehicles and edge servers. In addition, this paper proposes a DRL-based task offloading scheme to obtain better task caching and offloading strategies. The simulation results show that the scheme proposed in this article performs better compared to other schemes and effectively reduces the latency and energy consumption of task processing. Mobile edge computing Internet of Vehicles Deep reinforcement learning Content caching Task offloading Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2024 Read the published version in Peer-to-Peer Networking and Applications → Version 1 posted Editorial decision: Revision requested 15 Jun, 2024 Reviews received at journal 03 Jun, 2024 Reviews received at journal 19 May, 2024 Reviewers agreed at journal 06 May, 2024 Reviewers agreed at journal 06 May, 2024 Reviewers invited by journal 06 May, 2024 Editor assigned by journal 02 May, 2024 Submission checks completed at journal 26 Feb, 2024 First submitted to journal 20 Feb, 2024 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. 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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-3972282","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274953308,"identity":"bfe3d976-33ac-4be4-be1c-55e387f72312","order_by":0,"name":"Huizi Tian","email":"","orcid":"","institution":"Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"Huizi","middleName":"","lastName":"Tian","suffix":""},{"id":274953309,"identity":"28fdcbf2-60c1-4469-b6e4-e23cf06f18d7","order_by":1,"name":"Lin Zhu","email":"","orcid":"","institution":"Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Zhu","suffix":""},{"id":274953310,"identity":"a1f7a11c-69e4-458b-906c-3746b0f0bd42","order_by":2,"name":"Long Tan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYHACxgNAQs6AnSEBSDMTVs8DxCAtxgbMpGpJ3ABRTIQWe/azBw583FGbvp2Z4ZkEQ4V1YgNQBL8tPHkJB2eeOZ67s5khTYLhTHpiA1CEgMNyDA7zth3L3XAYqIWx7XBigwSPAX4t/G/AWtINwFr+EaNFAmxLTQJESwMxWm68MTg4s+2AIdBhyRYJx9KN23hy8Gth788xfPCxrU7e4HhP4o0PNday/exn8GuBgsMgCxPAkclGjHogqANZeIBIxaNgFIyCUTDSAAAq+kVZjGHtagAAAABJRU5ErkJggg==","orcid":"","institution":"Heilongjiang University","correspondingAuthor":true,"prefix":"","firstName":"Long","middleName":"","lastName":"Tan","suffix":""}],"badges":[],"createdAt":"2024-02-20 08:59:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3972282/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3972282/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12083-024-01836-2","type":"published","date":"2024-12-23T15:57:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72640855,"identity":"7d3fef2d-3a40-4c94-8ad0-b3496f3d09f5","added_by":"auto","created_at":"2024-12-30 16:10:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":740226,"visible":true,"origin":"","legend":"","description":"","filename":"HuiziTian.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3972282/v1_covered_ee239f59-c430-45de-9281-67aaf82d810e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A joint task caching and computation offloading scheme based on Deep Reinforcement Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"peer-to-peer-networking-and-applications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ppna","sideBox":"Learn more about [Peer-to-Peer Networking and Applications](http://link.springer.com/journal/12083)","snPcode":"12083","submissionUrl":"https://submission.nature.com/new-submission/12083/3","title":"Peer-to-Peer Networking and Applications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Mobile edge computing, Internet of Vehicles, Deep reinforcement learning, Content caching, Task offloading","lastPublishedDoi":"10.21203/rs.3.rs-3972282/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3972282/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Considering the dynamic variability of the vehicular edge environment and the limited edge servers resources, this paper proposes a joint task caching and computation offloading scheme based on deep reinforcement learning (DRL). 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