Mr. Annapurneswar Putrevu

Reinforcement Learning for Autonomous Self-Healing Data Pipelines in Cloud-Native Systems

Abstract:

Modern enterprises rely heavily on complex data integration pipelines that are increasingly vulnerable to schema drift, resource contention, and unpredictable anomalies. These challenges often lead to downtime, costly manual interventions, and reduced trust in data systems. Traditional rule-based recovery mechanisms are insufficient in dynamic, distributed, and cloud- native environments, where adaptability and resilience are essential.

This paper proposes an autonomous self-healing framework for data pipelines powered by Reinforcement Learning (RL). The system employs RL agents trained on historical telemetry, streaming metrics, and fault injection scenarios to learn recovery strategies. By continuously monitoring pipeline behavior, the agents dynamically reallocate resources, adjust ETL workflows, remap schemas, and apply corrective actions such as targeted retries and adaptive  backpressure management.

The framework is deployed in Kubernetes-based CI/CD environments with containerized microservices, anomaly detection models, and predictive analytics. This architecture enables real-time fault diagnosis, proactive remediation, and continuous optimization without constant human oversight. Experimental evaluations under high-ingestion workloads and stress tests demonstrate significant reductions in Mean Time to Recovery (MTTR) and improved adherence to Service Level Objectives (SLOs).

Beyond operational efficiency, the approach represents a paradigm shift toward policy-driven, intelligent, and self-managing data pipelines. Its modular design ensures compatibility with heterogeneous data sources, API-driven integration frameworks, and multi-cloud deployments. By combining RL-driven decision-making with continuous feedback loops, this framework delivers a practical blueprint for building resilient, fault-tolerant, and adaptive data systems capable of sustaining enterprise-scale demands.

Profile:

Annapurneswar Putrevu is a seasoned Enterprise Architect and Senior Manager with over 22 years of experience designing and delivering enterprise IT solutions across ERP, CRM, supply chain, manufacturing, and cloud platforms. At Bloom Energy, he leads enterprise architecture, middleware, and quality assurance initiatives, focusing on building scalable, secure, and data-driven systems.

Annapurneswar architected the AMG Financiers Public Portal(For his organization's customers), automating compliance tracking and delivering substantial annual savings while enhancing transparency for customers and financiers. He is currently implementing Ask EBS, a GenAI-powered solution that enables natural language access to Oracle ERP data, projected to improve data retrieval speed by 60% and empower business users with SQL-free insights. 

His expertise spans architecting enterprise data platforms with robust governance, developing API frameworks, and implementing ML-driven forecasting systems that have improved accuracy and reduced costs. Annapurneswar has also designed large-scale ERP and Salesforce integrations, as well as compliance automation platforms that have delivered significant operational efficiencies.

Earlier in his career, he served as a Principal Consultant at Oracle, delivering over 200 enterprise integrations for global clients, and held senior consulting roles in both the U.S. and India. He holds a Master of Computer Applications from NIT Raipur, an Advanced Certification in Software Engineering from IIT Madras, and a Postgraduate Certificate in AI/ML from the University of Texas McCombs School of Business. Annapurneswar is a Globee Awards Technology Judge and a published author, recognized for his leadership in enterprise digital transformation.