EcoTaskSched: A hybrid machine learning approach for energy-efficient task scheduling in IoT-based Fog-Cloud environments

EcoTaskSched: A hybrid machine learning approach for energy-efficient task scheduling in IoT-based Fog-Cloud environments | 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 Article EcoTaskSched: A hybrid machine learning approach for energy-efficient task scheduling in IoT-based Fog-Cloud environments Asfandyar Khan, Faizan Ullah, Dilawar Shah, Muhammad Haris Khan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5775826/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Background The widespread adoption of cloud services has posed several challenges, primarily revolving around energy and resource efficiency. Integrating cloud and fog resources can help address these challenges by improving fog-cloud computing environments. Nevertheless, the search for optimal task allocation and energy management in such environments continues. Existing studies have introduced notable solutions; however, it is still a challenging issue to efficiently utilize these heterogeneous cloud resources and achieve energy-efficient task scheduling in fog-cloud of things environment. Objective To tackle these challenges, we propose a novel ML-based EcoTaskSched model, which leverages deep learning for energy-efficient task scheduling in fog-cloud networks. The proposed hybrid model integrates Convolutional Neural Networks (CNNs) with Bidirectional Log-Short Term Memory (BiLSTM) to enhance energy-efficient schedulability and reduce energy usage while ensuring QoS provisioning. The CNN model efficiently extracts workload features from tasks and resources, while the BiLSTM captures complex sequential information, predicting optimal task placement sequences. Methods A real fog-cloud environment is implemented using the COSCO framework for the simulation setup together with four physical nodes from the Azure B2s plan to test the proposed model. The DeFog benchmark is used to develop task workloads, and data collection was conducted for both normal and intense workload scenarios. Before preprocessing the data was normalized, treated with feature engineering and augmentation, and then split into training and test sets. Results To evaluate performance, the proposed EcoTaskSched model demonstrated superiority by significantly reducing energy consumption and improving job completion rates compared to baseline models. Additionally, the EcoTaskSched model maintained a high job completion rate of 85%, outperforming GGCN and BiGGCN. It also achieved a lower average response time, and SLA violation rates, as well as increased throughput, and reduced execution cost compared to other baseline models. Conclusion and Future Work: In its optimal configuration, the EcoTaskSched model is successfully applied to fog-cloud computing environments, increasing task handling efficiency and reducing energy consumption while maintaining the required QoS parameters. Our future studies will focus on long-term testing of the EcoTaskSched model in real-world IoT environments. We will also assess its applicability by integrating other ML models, which could provide enhanced insights for optimizing scheduling algorithms across diverse fog-cloud settings. Physical sciences/Mathematics and computing Physical sciences/Physics AI Cloud Convolutional Neural Networks Fog IoT ML SLA Task scheduling QoS Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 01 Apr, 2025 Reviews received at journal 01 Apr, 2025 Reviews received at journal 27 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers invited by journal 27 Mar, 2025 Submission checks completed at journal 26 Mar, 2025 First submitted to journal 22 Mar, 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. 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-5775826","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":434743208,"identity":"1143e821-a97c-4fbc-a7bf-237199850977","order_by":0,"name":"Asfandyar Khan","email":"","orcid":"","institution":"Hazara University Mansehra","correspondingAuthor":false,"prefix":"","firstName":"Asfandyar","middleName":"","lastName":"Khan","suffix":""},{"id":434743209,"identity":"5d0d1586-38ea-4e5f-8f5c-6be2b717a02a","order_by":1,"name":"Faizan Ullah","email":"","orcid":"","institution":"Bacha Khan University","correspondingAuthor":false,"prefix":"","firstName":"Faizan","middleName":"","lastName":"Ullah","suffix":""},{"id":434743211,"identity":"7be7e3b2-1a93-41d2-bdfc-0ab6eab97640","order_by":2,"name":"Dilawar Shah","email":"","orcid":"","institution":"Bacha Khan University","correspondingAuthor":false,"prefix":"","firstName":"Dilawar","middleName":"","lastName":"Shah","suffix":""},{"id":434743212,"identity":"cb37f544-39d9-4d48-9cfd-d20c8aa6f8f7","order_by":3,"name":"Muhammad Haris Khan","email":"","orcid":"","institution":"Bacha Khan University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Haris","lastName":"Khan","suffix":""},{"id":434743214,"identity":"cecf7837-4efe-4e06-8ab6-25dfe489bde8","order_by":4,"name":"Shujaat Ali","email":"","orcid":"","institution":"Bacha Khan University","correspondingAuthor":false,"prefix":"","firstName":"Shujaat","middleName":"","lastName":"Ali","suffix":""},{"id":434743216,"identity":"f0ee88d8-3e92-43f5-848c-448f038cdad2","order_by":5,"name":"Muhammad Tahir","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYJACZgYDBjkJIEMCzD0AxBUENIC0GKNqOUNQCwND4gyiteg28B/8XFBwL31m+9mHN37uYJDju5HA+OEAHi1mB5iZpWcYFOfO5kk3tuw9w2AseSOBWYKAFgZpHoOE3HkMaWwSvG0MiRtuJDBIfyBgy2+glnQ5/mdskn/bGOqBWph/ELCFDWRLgrREGps00JYEgxsJbPgddpjZzBqoxXDmjGfM1rJtEoYzzzxss8Cr5Xjj49s8fxLkJc6nMd5822Yjz3c8+fANfFrAkYIEQFHD2IBPwygYBaNgFIwCIgAAM8pImtVN0tAAAAAASUVORK5CYII=","orcid":"","institution":"Kardan University","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Tahir","suffix":""}],"badges":[],"createdAt":"2025-01-06 18:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5775826/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5775826/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-96974-9","type":"published","date":"2025-04-10T16:04:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80559255,"identity":"28bde5d9-e5fd-4f5a-8f54-7b86508dc0a8","added_by":"auto","created_at":"2025-04-14 16:18:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1168609,"visible":true,"origin":"","legend":"","description":"","filename":"MainFile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5775826/v1_covered_1e2906c7-770b-47df-8d6b-badcef016ba4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"EcoTaskSched: A hybrid machine learning approach for energy-efficient task scheduling in IoT-based Fog-Cloud environments","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"AI, Cloud, Convolutional Neural Networks, Fog, IoT, ML, SLA, Task scheduling, QoS","lastPublishedDoi":"10.21203/rs.3.rs-5775826/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5775826/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe widespread adoption of cloud services has posed several challenges, primarily revolving around energy and resource efficiency. Integrating cloud and fog resources can help address these challenges by improving fog-cloud computing environments. Nevertheless, the search for optimal task allocation and energy management in such environments continues. Existing studies have introduced notable solutions; however, it is still a challenging issue to efficiently utilize these heterogeneous cloud resources and achieve energy-efficient task scheduling in fog-cloud of things environment.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo tackle these challenges, we propose a novel ML-based EcoTaskSched model, which leverages deep learning for energy-efficient task scheduling in fog-cloud networks. The proposed hybrid model integrates Convolutional Neural Networks (CNNs) with Bidirectional Log-Short Term Memory (BiLSTM) to enhance energy-efficient schedulability and reduce energy usage while ensuring QoS provisioning. The CNN model efficiently extracts workload features from tasks and resources, while the BiLSTM captures complex sequential information, predicting optimal task placement sequences.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA real fog-cloud environment is implemented using the COSCO framework for the simulation setup together with four physical nodes from the Azure B2s plan to test the proposed model. The DeFog benchmark is used to develop task workloads, and data collection was conducted for both normal and intense workload scenarios. Before preprocessing the data was normalized, treated with feature engineering and augmentation, and then split into training and test sets.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTo evaluate performance, the proposed EcoTaskSched model demonstrated superiority by significantly reducing energy consumption and improving job completion rates compared to baseline models. Additionally, the EcoTaskSched model maintained a high job completion rate of 85%, outperforming GGCN and BiGGCN. It also achieved a lower average response time, and SLA violation rates, as well as increased throughput, and reduced execution cost compared to other baseline models.\u003c/p\u003e\u003ch2\u003eConclusion and Future Work:\u003c/h2\u003e \u003cp\u003eIn its optimal configuration, the EcoTaskSched model is successfully applied to fog-cloud computing environments, increasing task handling efficiency and reducing energy consumption while maintaining the required QoS parameters. Our future studies will focus on long-term testing of the EcoTaskSched model in real-world IoT environments. We will also assess its applicability by integrating other ML models, which could provide enhanced insights for optimizing scheduling algorithms across diverse fog-cloud settings.\u003c/p\u003e","manuscriptTitle":"EcoTaskSched: A hybrid machine learning approach for energy-efficient task scheduling in IoT-based Fog-Cloud environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-28 04:23:53","doi":"10.21203/rs.3.rs-5775826/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-04-01T12:27:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-01T06:53:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-27T09:51:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98178326462931127611936859248775510435","date":"2025-03-27T08:46:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59625015777764144439234791414950823373","date":"2025-03-27T08:37:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112695370180515839797353703824087783623","date":"2025-03-27T08:37:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-27T08:33:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-26T19:41:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-22T14:59:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c9600893-043f-475f-8a3c-34e7f815b7b6","owner":[],"postedDate":"March 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46290332,"name":"Physical sciences/Mathematics and computing"},{"id":46290333,"name":"Physical sciences/Physics"}],"tags":[],"updatedAt":"2025-04-14T16:17:08+00:00","versionOfRecord":{"articleIdentity":"rs-5775826","link":"https://doi.org/10.1038/s41598-025-96974-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-10 16:04:57","publishedOnDateReadable":"April 10th, 2025"},"versionCreatedAt":"2025-03-28 04:23:53","video":"","vorDoi":"10.1038/s41598-025-96974-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-96974-9","workflowStages":[]},"version":"v1","identity":"rs-5775826","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5775826","identity":"rs-5775826","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}