A Self-Evolving, Reinforcement Learning-Driven Architecture for Cost-Aware and Self-Healing Real-Time Data Pipelines

A Self-Evolving, Reinforcement Learning-Driven Architecture for Cost-Aware and Self-Healing Real-Time Data Pipelines | 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 A Self-Evolving, Reinforcement Learning-Driven Architecture for Cost-Aware and Self-Healing Real-Time Data Pipelines Venkat Alamuri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9455617/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 Dynamic data distributions, system failures, and low-latency, cost-effective processing are becoming more of a challenge to modern real-time data pipelines. Current streaming architectures are based on relatively static settings and reactive processes and are thus not capable of responding to changing conditions and ensuring consistent performance. In this paper, a smart and responsive framework that combines real-time validation, predictive anomaly detection and autonomous recovery, and learning-based optimization is proposed in one pipeline architecture. The suggested solution allows to maintain constant monitoring and decision making with the help of the closed-loop system balancing dynamically the latency, computational cost, and the quality of the data. An integrated verification system maximizes the effectiveness of anomaly detection and an independent recovery system guarantees the quick recovery of faults and the recovery of the system with the lowest downtimes. Experimental assessment of various situations proves to be dramatically better than traditional systems with lower latency diversity, shorter recovery period, greater detection precision, and more economical in limited situations. The findings indicate the possibility of turning the conventional reactive pipelines into proactive and self-adaptive systems, which provides a scalable and robust solution to the current data-intensive applications like financial analytics, IoT monitoring, and large-scale cloud environments. Artificial Intelligence and Machine Learning Computer Architecture and Engineering Self-healing data pipelines Reinforcement learning Stream processing Cost-aware optimization Real-time validation Adaptive systems Anomaly detection Full Text Additional Declarations The authors declare no competing interests. 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. 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