Toward Adaptive Workload Scheduling in Kubernetes Across the Edge-Cloud Continuum

Toward Adaptive Workload Scheduling in Kubernetes Across the Edge-Cloud Continuum | 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 Toward Adaptive Workload Scheduling in Kubernetes Across the Edge-Cloud Continuum Vid Bregar, Matjaz B. Juric This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7555016/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract As the digital landscape evolves, the demand for high-performance, low-latency computing has surged, pushing traditional cloud computing to its limits. This has led to the conceptualization of the edge-cloud continuum, which integrates and leverages the strengths of the cloud, fog, and end-devices layers. However, realizing this continuum remains a challenge. This paper presents an architecture and evaluation of a Kubernetes-based platform that provides seamless integration between the cloud and fog layers. Key contributions include the reasoning for choosing Kubernetes as the base, a comparative analysis of single versus multi-cluster Kubernetes architectures, federation solutions, and cluster network interconnection strategies. The introduction of workload descriptors enables users to convey the edge-cloud continuum requirements and preferences of various workloads to the platform. By integrating the CosmoSpan Scheduler with the CosmoSpan Data Collector and workload descriptors, workload instance scheduling is significantly improved compared to the standard Kubernetes scheduler. This is demonstrated by a 46% improvement in the average Edge-Cloud Continuum Placement Score without sacrificing scheduling time. This score evaluates factors such as CPU, memory, node uptime, network bandwidth, latency, fault tolerance, and unscheduled workload instances. Additionally, CosmoSpan Deschedulers maintain a high and stable Edge-Cloud Continuum Placement Score despite environmental changes, ensuring that edge-cloud continuum objectives are met at all times. Edge-cloud continuum Platform architecture Kubernetes Multi-cluster Workload scheduling Workload rescheduling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 14 May, 2026 Reviews received at journal 11 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviews received at journal 10 Feb, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers invited by journal 16 Sep, 2025 Editor assigned by journal 16 Sep, 2025 Submission checks completed at journal 09 Sep, 2025 First submitted to journal 07 Sep, 2025 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-7555016","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517182427,"identity":"020529a0-9e5f-4509-bdbb-0333cc970720","order_by":0,"name":"Vid Bregar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYDACCfaDj//8sAExDSAizAyMByQYGHhwa+FJNuDtSQOpAGpJAGthIKCFwUyAh+0wkhYgOIDPXQa3G9KANp23t5dI3vi48AdD4nZ2oBYLhjoZnFruHDz2wMDidmKPRFqx8YwEhsSdzWCHHcbpMMkZCekGCTy3E3gkcsykeYBaNhzm/wDUcgCfFjOJA2zn7IFazH9DtIBtqcOphV8iwUyyge0AYw/QFmYkLcy4tcicSTZm7ElO7DnzrFiaJ03CGKLFALdf2KTbDz5m+GFnz96evPEzj42N7IbzBxgfS1TU2ePSgg4kwCSzhAGxGmCA8QOpOkbBKBgFo2A4AwCC6k4E4noMbQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Ljubljana","correspondingAuthor":true,"prefix":"","firstName":"Vid","middleName":"","lastName":"Bregar","suffix":""},{"id":517182428,"identity":"f9b01d8f-8cbd-4897-a7ad-4385410967c9","order_by":1,"name":"Matjaz B. 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