Hierarchical service chain orchestration for multi-cloud environments enabled by deep reinforcement learning

Hierarchical service chain orchestration for multi-cloud environments enabled by 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 Hierarchical service chain orchestration for multi-cloud environments enabled by deep reinforcement learning Yuncheng Xie, Kehe Wu, Yuan Jiang, Xiaoliang Zhang, Wenchao Cui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8497490/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2026 Read the published version in Journal of Cloud Computing → Version 1 posted 16 You are reading this latest preprint version Abstract With the rapid adoption of multi-cloud platforms, dynamic orchestration of service function chains faces coupled challenges. This study proposes a hierarchical service chain orchestration for multi-cloud environments, which decomposes end-to-end quality of service into real-time, economic, and stability objectives. This model uses hierarchical orchestration structure and integrates Actor-Critic networks with Graph Isomorphism Network and attention mechanisms. The bandwidth allocation layer optimizes bandwidth coefficients for deadline-sensitive tasks, while the server selection layer processes dynamic effective subgraphs for cost-aware server deployment. By Filtering invalid resource nodes via multidimensional constraints to reduce computational load, and prioritizes user requests using a weighted scoring model. Validated on a real-world cloud platform, our method achieves remarkable performance improvements across different load scenarios. Compared with the optimal baseline, it attains a comprehensive score enhancement of 2.56%, 3.44%, and 16.8% under idle, normal, and stress states, respectively. Meanwhile, it achieves a reduction of 0.25%, 0.35%, and 0.45% in the server overload percentage corresponding to the aforementioned scenarios. The model enables flexible and efficient service chain orchestration in multi-cloud. service function chain multi-cloud orchestration quality of service deep reinforcement learning (DRL) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Apr, 2026 Read the published version in Journal of Cloud Computing → Version 1 posted Editorial decision: Revision requested 24 Jan, 2026 Reviews received at journal 21 Jan, 2026 Reviews received at journal 19 Jan, 2026 Reviews received at journal 18 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers agreed at journal 10 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers invited by journal 07 Jan, 2026 Editor assigned by journal 03 Jan, 2026 Submission checks completed at journal 02 Jan, 2026 First submitted to journal 01 Jan, 2026 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-8497490","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571807585,"identity":"59c855bd-b74e-4919-a16e-58246263904f","order_by":0,"name":"Yuncheng Xie","email":"","orcid":"","institution":"North China Electric Power University","correspondingAuthor":false,"prefix":"","firstName":"Yuncheng","middleName":"","lastName":"Xie","suffix":""},{"id":571807586,"identity":"946ca123-2d2c-47bb-b048-b93601661f9a","order_by":1,"name":"Kehe Wu","email":"","orcid":"","institution":"North China Electric Power 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