Contention-aware Greedy Heuristic Method and Learning based Method for Load Balancing through Scheduling for Containers in Cloud Computing Environments

Contention-aware Greedy Heuristic Method and Learning based Method for Load Balancing through Scheduling for Containers in Cloud Computing 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 Contention-aware Greedy Heuristic Method and Learning based Method for Load Balancing through Scheduling for Containers in Cloud Computing Environments Neelima Gogineni, Saravanan. M. S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4180411/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 Containerization became indispensable in distributed environments for packaging software and dependencies in a lightweight executable container. In the era of big data and availability of cloud infrastructure, it is more so as distributed applications are more resource and data-intensive. Such High Performance Computing (HPC) applications are deployed in containerized services. However, data-intensive nature of those applications lead to poor performance unless scheduler considers it. In this paper, not only load balancing of containers but also performance of underlying applications is considered. Towards this end, a scheduling algorithm with unified optimization considering load balancing and also application performance is proposed. This algorithm, named Contention-aware Greedy Heuristic Scheduling and Load Balancing for Containers (CGHSLBC), helps in improving performance of containerization in distributed environments. Problems associated with containerization in terms of balancing load and also application performance are NP-hard. CGHSLBC has heuristics to deal with such issues. Empirical study has revealed that CGHSLBC better application performance besides balancing load of containerized services in cloud infrastructure. We also proposed a learning based methodology to schedule and balance load for containers. It is based on Deep Reinforcement Learning (DRL) where state change is continuously monitored while making well informed scheduling decisions. An algorithm named Reinforcement Learning based Dynamic Scheduling (RLbDS) is proposed and empirical study has revealed that it shows better performance over state of the art methods. Cloud Computing Containerization High Performance Computing Scheduling Load Balancing Deep Reinforcement Learning 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. 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-4180411","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":289258072,"identity":"50ee3cae-d9e7-417d-8302-de9019e1ca6f","order_by":0,"name":"Neelima Gogineni","email":"data:image/png;base64,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","orcid":"","institution":"Saveetha Institute of Medical and Technical Sciences Chennai","correspondingAuthor":true,"prefix":"","firstName":"Neelima","middleName":"","lastName":"Gogineni","suffix":""},{"id":289258073,"identity":"18d7210b-df97-4e58-9870-497db72e9ab4","order_by":1,"name":"Saravanan. 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