A Adaptive Service Deployment Algorithm for Cloud-Edge Collaborative system based on Speedup Weights

A Adaptive Service Deployment Algorithm for Cloud-Edge Collaborative system based on Speedup Weights | 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 Adaptive Service Deployment Algorithm for Cloud-Edge Collaborative system based on Speedup Weights Zhichao Hu, Sheng Chen, Huanle Rao, Chenjie Hong, Ouhan Huang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4166951/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract In the contemporary landscape of edge computing, the deployment of services with stringent real-time requirements on edge devices is increasingly prevalent. However, the challenge of designing an effective service deployment strategy that optimally leverages both cloud and edge resources to deliver high-quality services in production environments persists, primarily due to resource constraints in edge devices. To tackle this issue, we introduce an adaptive service deployment algorithm that utilizes speedup weights for cloud-edge collaborative environments(SWD-AD). This algorithm is crafted by comparing task execution times in both cloud and edge settings and integrating Speedup Weights with resource consumption metrics. During task cluster operations, service-specific task processing data is collected, and cumulative Speedup Weights are computed. Based on this metric, a dynamic service adjustment policy is implemented to facilitate service migration between cloud and edge, optimizing resource allocation. Our performance evaluation experiments reveal that this strategy notably reduces the average response time of tasks by 29.38% and 25.86% compared to Swarm and kubernetes (K8s) algorithms, respectively. Edge computing Cloud-edge collaboration Service deployment Speedup weights Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Apr, 2024 Reviews received at journal 20 Apr, 2024 Reviews received at journal 14 Apr, 2024 Reviews received at journal 11 Apr, 2024 Reviews received at journal 11 Apr, 2024 Reviews received at journal 07 Apr, 2024 Reviewers agreed at journal 31 Mar, 2024 Reviewers agreed at journal 31 Mar, 2024 Reviewers agreed at journal 31 Mar, 2024 Reviewers agreed at journal 31 Mar, 2024 Reviewers agreed at journal 31 Mar, 2024 Reviewers agreed at journal 31 Mar, 2024 Reviewers invited by journal 31 Mar, 2024 Editor assigned by journal 27 Mar, 2024 Submission checks completed at journal 26 Mar, 2024 First submitted to journal 26 Mar, 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. 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-4166951","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":284336165,"identity":"5ba88714-6857-421a-9345-0e396e9f600b","order_by":0,"name":"Zhichao Hu","email":"","orcid":"","institution":"Hangzhou Dianzi University","correspondingAuthor":false,"prefix":"","firstName":"Zhichao","middleName":"","lastName":"Hu","suffix":""},{"id":284336166,"identity":"2af79f71-36f1-4564-8bda-ee0f571acad3","order_by":1,"name":"Sheng Chen","email":"","orcid":"","institution":"Jianghuai Advance Technology Center","correspondingAuthor":false,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Chen","suffix":""},{"id":284336168,"identity":"80e42dfd-57f1-461c-8389-80c501257e69","order_by":2,"name":"Huanle Rao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYBACA3YQWXGAsQFISRCnhRlEniFZC2MbKVrMmXkffvg4747shgPMB2/zMNjlEdRi2cxuLDlz2zPjDQfYkq15GJKLCTvsMBuDNO+2w4kbDvCYSfMwHEhsIEIL82/eOSAt/N+I1sImzdsAtoWNeC2WM44dNp55mM3Yco5BMhFajrcx3/hQc1i273jzwxtvKuwIa0EAcJwaEK9+FIyCUTAKRgEeAACnQDwoNJjbZgAAAABJRU5ErkJggg==","orcid":"","institution":"Hangzhou Dianzi University","correspondingAuthor":true,"prefix":"","firstName":"Huanle","middleName":"","lastName":"Rao","suffix":""},{"id":284336172,"identity":"0ee45bb6-8428-4ffd-a7fb-855af5d99103","order_by":3,"name":"Chenjie Hong","email":"","orcid":"","institution":"Hangzhou Dianzi University","correspondingAuthor":false,"prefix":"","firstName":"Chenjie","middleName":"","lastName":"Hong","suffix":""},{"id":284336175,"identity":"481c79dd-04ba-490e-b590-1eaa99d3a850","order_by":4,"name":"Ouhan Huang","email":"","orcid":"","institution":"Hangzhou Dianzi University","correspondingAuthor":false,"prefix":"","firstName":"Ouhan","middleName":"","lastName":"Huang","suffix":""},{"id":284336178,"identity":"7f08470c-1dcf-4f3c-98d5-ce6c3134ad79","order_by":5,"name":"Xiaobin Xu","email":"","orcid":"","institution":"Hangzhou Dianzi University","correspondingAuthor":false,"prefix":"","firstName":"Xiaobin","middleName":"","lastName":"Xu","suffix":""},{"id":284336181,"identity":"97615f4a-9963-492c-9d46-79d99ea72358","order_by":6,"name":"Gangyong Jia","email":"","orcid":"","institution":"Hangzhou Dianzi University","correspondingAuthor":false,"prefix":"","firstName":"Gangyong","middleName":"","lastName":"Jia","suffix":""}],"badges":[],"createdAt":"2024-03-26 04:44:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4166951/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4166951/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53710184,"identity":"63217f0a-2c7c-4184-9256-644d4a5fd49d","added_by":"auto","created_at":"2024-03-29 07:52:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1007013,"visible":true,"origin":"","legend":"","description":"","filename":"SWDADTheJournalofSupercomputing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4166951/v1_covered_72cd66f9-7ae4-413e-b44e-a5b3221d2c4a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Adaptive Service Deployment Algorithm for Cloud-Edge Collaborative system based on Speedup Weights","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":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Edge computing, Cloud-edge collaboration, Service deployment, Speedup weights","lastPublishedDoi":"10.21203/rs.3.rs-4166951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4166951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the contemporary landscape of edge computing, the deployment of services with stringent real-time requirements on edge devices is increasingly prevalent. However, the challenge of designing an effective service deployment strategy that optimally leverages both cloud and edge resources to deliver high-quality services in production environments persists, primarily due to resource constraints in edge devices. To tackle this issue, we introduce an adaptive service deployment algorithm that utilizes speedup weights for cloud-edge collaborative environments(SWD-AD). This algorithm is crafted by comparing task execution times in both cloud and edge settings and integrating Speedup Weights with resource consumption metrics. During task cluster operations, service-specific task processing data is collected, and cumulative Speedup Weights are computed. Based on this metric, a dynamic service adjustment policy is implemented to facilitate service migration between cloud and edge, optimizing resource allocation. Our performance evaluation experiments reveal that this strategy notably reduces the average response time of tasks by 29.38\\% and 25.86\\% compared to Swarm and kubernetes (K8s) algorithms, respectively.","manuscriptTitle":"A Adaptive Service Deployment Algorithm for Cloud-Edge Collaborative system based on Speedup Weights","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-29 07:44:05","doi":"10.21203/rs.3.rs-4166951/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-26T15:00:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-20T20:13:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-14T17:18:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-12T03:57:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-11T19:23:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-08T00:33:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53333887-7c3e-4c2e-be9f-db592fc421bb_SNPRID","date":"2024-04-01T00:16:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"501dfbe4-1329-4fc4-9c12-e4a48df284a5","date":"2024-03-31T18:53:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98c50a12-eba3-4324-ba42-466c1276c412","date":"2024-03-31T18:14:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59a19e27-aa37-44c3-a994-97e83a060502","date":"2024-03-31T16:56:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"a38be598-5056-4cea-a836-bd813fbdeffd","date":"2024-03-31T16:26:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4314cfb7-e337-4901-88d3-1532bbf72337","date":"2024-03-31T16:22:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-31T15:40:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-27T04:03:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-27T03:28:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Journal of Supercomputing","date":"2024-03-26T04:42:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d7cc5319-d5be-49a7-b760-70024083f5cd","owner":[],"postedDate":"March 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-01T23:10:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-29 07:44:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4166951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4166951","identity":"rs-4166951","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}