[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82302-en":3,"doc-seo-82302-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82302,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","EcoKube Simulating Carbon-Aware Scheduling Policies in Heterogeneous Edge–Cloud Environments","Energy demand from cloud and edge computing is increasing quickly, and AI workloads intensify electricity use and associated carbon emissions. In hybrid edge–cloud deployments, sustainability outcomes depend on time- and location-varying grid Carbon Intensity (CI), site power efficiency via PUE, and heterogeneous hardware characteristics. Existing approaches lack a consistent, reproducible workflow for evaluating sustainability-aware scheduling across heterogeneous, federated topologies. EcoKube provides a configurable, deterministic event-driven simulator with policy hooks and a heterogeneity-aware reference policy for reproducible comparisons.","EcoKube: Simulating Carbon-Aware Scheduling Policies in Heterogeneous Edge–Cloud Environments  \narXiv :2607 .09318v1 [ cs .DC] 10 Jul 2026  \nGonçalo Ferreira  \n[g.j.teixeiradepinhoferreira@uva.nl](g.j.teixeiradepinhoferreira@uva.nl)[ ](g.j.teixeiradepinhoferreira@uva.nl)University of Amsterdam  \nAmsterdam, Netherlands  \nAbstract  \nEnergy demand from cloud and edge computing is rising rapidly, with AI workloads further intensifying electricity use and associated carbon emissions. In hybrid edge–cloud settings, sustainability impact depends on time-and location-varying grid Carbon Intensity (CI), site Power Usage Effectiveness (PUE), and heterogeneous hardware characteristics. Existing carbon-aware work explores solutions such as temporal elasticity, spatio-temporal workload shifting, and carbon-aware placement across distributed sites. However, these solutions do not provide a consistent and reproducible workflow for evaluating sustainability-aware scheduling policies on heterogeneous, federated edge–cloud topologies. We present EcoKube: a configurable simulation framework for the reproducible evaluation of sustainability-aware scheduling policies in heterogeneous edge–cloud environments. The framework includes an event-driven deterministic simulator, policy hooks, and a heterogeneity-aware reference policy. We evaluate the framework with synthetic batch workloads, comparing the reference policy against the default Kubernetes scheduler, KEIDS, and TOPSIS/KCSS. The contribution is architectural and experimental: EcoKube provides a reproducible way to compare sustainability-aware policies before deployment.  \nCCS Concepts: • Computer systems organization → Distributed architectures; • Networks → Cloud computing; • Computing methodologies → Discrete-event simulation; • Hardware → Impact on the environment.  \nKeywords: Sustainable computing, carbon-aware computing, federated computing systems, Kubernetes scheduling, multi-objective optimisation, Carbon Intensity (CI)  \nACM Reference Format:  \nGonçalo Ferreira and Shashikant Ilager. 2026. EcoKube: Simulating CarbonAware Scheduling Policies in Heterogeneous Edge–Cloud Environments. In 4th International Workshop on Testing Distributed Internet of Things Systems (TDIS ’26), April 27–30, 2026, Edinburgh, Scotland Uk. ACM, New York, NY, USA, 6 pages. [https://doi.org/10.1145/3802513.3803486](https://doi.org/10.1145/3802513.3803486)  \n1 Introduction  \nGlobal warming, largely driven by carbon dioxide emissions, remains one of the most pressing challenges of our time [15] . Atthe same time, compute demand keeps ballooning: modern AI/ML training and inference, data-intensive science, and always-on edge services push more workloads across a widening cloud–edge continuum [7] . This shift increases operational complexity, as execution  \nThis work is licensed under a Creative Commons Attribution 4 .0 International License. TDIS ’26, Edinburgh, Scotland Uk  \n© 2026 Copyright held by the owner/author(s) .  \n[https://doi.org/10.1145/3802513.3803486](https://doi.org/10.1145/3802513.3803486)  \nShashikant Ilager  \n[s.s.ilager@uva.nl](s.s.ilager@uva.nl)  \nUniversity of Amsterdam  \nAmsterdam, Netherlands  \nspans geo-distributed sites with different electricity mixes, facility overheads, and heterogeneous hardware, making sustainability impact more visible, but also more difficult to evaluate [1] .  \nA key approach for reducing operational footprint is through scheduling: deciding where and when workloads run, and how resources are allocated during execution. However, assessing the sustainability impact of scheduling policies is challenging in hybrid edge–cloud settings because carbon-and energy-related signals are multi-level and time-varying: grid carbon intensity changes by region and time; site efficiency (e.g., PUE) shifts with load and season; and node-level performance-per-watt depends on hardware generation and accelerator availability [16] .  \nSeveral state-of-the-art approaches explore carbon-a","cbCaik7tDrHrjqkE","https://ap.wps.com/l/cbCaik7tDrHrjqkE","pdf",634527,3,1,6,"English","en",105,"# Introduction\n## EcoKube framework and motivation\n## Evaluation methodology and policies","[{\"question\":\"Why is sustainability-aware scheduling challenging in heterogeneous edge–cloud environments?\",\"answer\":\"Sustainability signals are multi-level and time-varying, including region/time-varying grid carbon intensity, site-level efficiency (PUE) that changes with load/season, and node-level performance-per-watt affected by hardware generation and accelerator availability.\"},{\"question\":\"What is EcoKube and what problem does it address?\",\"answer\":\"EcoKube is a configurable simulation framework that enables a consistent, reproducible workflow for evaluating sustainability-aware scheduling policies in heterogeneous edge–cloud environments, while staying close to Kubernetes operational realities.\"},{\"question\":\"How does EcoKube support evaluating and comparing scheduling policies?\",\"answer\":\"It provides an event-driven deterministic simulator, site-level sustainability modelling (CI and normalization), node-level heterogeneity and feasibility constraints (latency and accelerator fit), and transparent policy hooks to instantiate and compare heuristics and multi-criteria schedulers.\"}]",1784179485,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"ecokube-simulating-carbon-aware-scheduling-policies-in-heterogeneous-edgecloud-environments","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/ecokube-simulating-carbon-aware-scheduling-policies-in-heterogeneous-edgecloud-environments/82302/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-21","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is sustainability-aware scheduling challenging in heterogeneous edge–cloud environments?","Question",{"text":75,"@type":76},"Sustainability signals are multi-level and time-varying, including region/time-varying grid carbon intensity, site-level efficiency (PUE) that changes with load/season, and node-level performance-per-watt affected by hardware generation and accelerator availability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is EcoKube and what problem does it address?",{"text":80,"@type":76},"EcoKube is a configurable simulation framework that enables a consistent, reproducible workflow for evaluating sustainability-aware scheduling policies in heterogeneous edge–cloud environments, while staying close to Kubernetes operational realities.",{"name":82,"@type":73,"acceptedAnswer":83},"How does EcoKube support evaluating and comparing scheduling policies?",{"text":84,"@type":76},"It provides an event-driven deterministic simulator, site-level sustainability modelling (CI and normalization), node-level heterogeneity and feasibility constraints (latency and accelerator fit), and transparent policy hooks to instantiate and compare heuristics and multi-criteria schedulers.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]