Reliability aware Task scheduler in Cloud Computing using improved AsynchronousAdvantage Actor critic(A3C) algorithm

Reliability aware Task scheduler in Cloud Computing using improved AsynchronousAdvantage Actor critic(A3C) algorithm | 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 Reliability aware Task scheduler in Cloud Computing using improved AsynchronousAdvantage Actor critic(A3C) algorithm Sudheer Mangalampalli, Ganesh Reddy Karri, Prasun Chakrabarti, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4417645/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Task Scheduling problem (TSP) in cloud computing is a critical aspect as diversified tasks from heterogeneous resources comes to cloud console. Mapping these diversified tasks to suitable virtual machines is challenge for the cloud service provider(CSP) to employ an efficient algorithm to tackle TSP. Ineffective scheduling lead to increase in makespan, failures which impacts reliability on CSP. Many authors developed various task scheduling mechanisms to tackle parameters makespan, execution time, energy consumption but very few authors addressed Rate of failures, reliability but there is need to optimize scheduling process in Cloud paradigm as it is a dynamic scenario. In this paper, a reliability aware task scheduler is formulated which calculates task priorities at task manager level to effectively schedule tasks. All priorities are fed to scheduler which is integrated with a deep Reinforcement learning model A3C which improved by adding RCNN to accelerate learning capacity and to extract features accurately mapping tasks to VMs according to their priorities. Simulations are carried out on Cloudsim using fabricated data distributions, real time worklogs. We evaluated our proposed RTSIA3C with baseline algorithms DQN, A2C. Results revealed that RTSIA3C outperformed over baseline approaches by minimizing makespan, rate of failures while improving reliability. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Cloud Computing Task Scheduling makespan Reliability Rate of Failures Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-4417645","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":302876350,"identity":"f9c66ceb-bbef-44a3-8ffc-33bf6cc3a8a7","order_by":0,"name":"Sudheer Mangalampalli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIiWNgGAWjYHACMxBhwAblJfCDyQKitDAzMBwAKpZsAGkxIEILA0yLwQEoHxeQn5G87cGHinvGfOz9Rzd/qLDLMz6/OvHDAwMGeX6xA1i1GNxIKzeccabYjI3nMNuNA2eSi81uvN0sAXSY4czZCdi1SOSYSfO2JdiwSSSz3TjYdiBx242zG0BaEgxuY9ciPwOo5e8/oBb5x0At/w4kbp5xdvMPfFoYbgC1MDYkmLFJMAO1NBxI3MDfuw2vLQZnnpVJ9hxLMGbjSTa7ceZYcuKMG7zbLBIMJHD6Rb49eZvEj5oEw/ntB5/dqKixS+zvP7v55o8KG3l+aRwOwwQSYJUSxCoHAf4DpKgeBaNgFIyCEQAAymRk+/MxSE0AAAAASUVORK5CYII=","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":true,"prefix":"","firstName":"Sudheer","middleName":"","lastName":"Mangalampalli","suffix":""},{"id":302876351,"identity":"439eeafe-08ff-4e01-bbfc-412fb870ca42","order_by":1,"name":"Ganesh Reddy Karri","email":"","orcid":"","institution":"VIT-AP University","correspondingAuthor":false,"prefix":"","firstName":"Ganesh","middleName":"Reddy","lastName":"Karri","suffix":""},{"id":302876352,"identity":"42610a78-73c2-4e1c-a035-b4725043431c","order_by":2,"name":"Prasun Chakrabarti","email":"","orcid":"","institution":"Sir Padampat Singhania University","correspondingAuthor":false,"prefix":"","firstName":"Prasun","middleName":"","lastName":"Chakrabarti","suffix":""},{"id":302876353,"identity":"acb5ea1f-c3a7-4b14-be84-55b04d4a4281","order_by":3,"name":"Tulika chakrabarti","email":"","orcid":"","institution":"Sir Padampat Singhania University Udaipur","correspondingAuthor":false,"prefix":"","firstName":"Tulika","middleName":"","lastName":"chakrabarti","suffix":""},{"id":302876354,"identity":"e01cf32b-baa7-4b3f-99ab-396ce451ee1a","order_by":4,"name":"Martin Margala","email":"","orcid":"","institution":"University of Louisiana at Lafayette","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Margala","suffix":""}],"badges":[],"createdAt":"2024-05-14 08:25:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4417645/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4417645/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67080052,"identity":"4429ec9b-cb32-4c3a-a78d-19ddefeff74a","added_by":"auto","created_at":"2024-10-21 04:40:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":983594,"visible":true,"origin":"","legend":"","description":"","filename":"RelSR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4417645/v1_covered_bc4fc2e1-6fd8-4b64-bf4e-48a0ddae928a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reliability aware Task scheduler in Cloud Computing using improved AsynchronousAdvantage Actor critic(A3C) algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cloud Computing, Task Scheduling, makespan, Reliability, Rate of Failures","lastPublishedDoi":"10.21203/rs.3.rs-4417645/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4417645/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTask Scheduling problem (TSP) in cloud computing is a critical aspect as diversified tasks from heterogeneous resources comes to cloud console. Mapping these diversified tasks to suitable virtual machines is challenge for the cloud service provider(CSP) to employ an efficient algorithm to tackle TSP. Ineffective scheduling lead to increase in makespan, failures which impacts reliability on CSP. Many authors developed various task scheduling mechanisms to tackle parameters makespan, execution time, energy consumption but very few authors addressed Rate of failures, reliability but there is need to optimize scheduling process in Cloud paradigm as it is a dynamic scenario. In this paper, a reliability aware task scheduler is formulated which calculates task priorities at task manager level to effectively schedule tasks. All priorities are fed to scheduler which is integrated with a deep Reinforcement learning model A3C which improved by adding RCNN to accelerate learning capacity and to extract features accurately mapping tasks to VMs according to their priorities. 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