Parameswara Reddy Nangi

Serverless Computing Optimization Strategies Using ML-Based Auto-Scaling and Event-Stream Intelligence for Low-Latency Enterprise Workloads

Abstract:

Serverless architectures promise scalability and cost efficiency; however, enterprise workloads often struggle with latency, unpredictable traffic patterns, and inefficient resource utilization. Traditional rule-based scaling mechanisms fail to adapt to highly dynamic, event-driven environments.

This talk explores advanced optimization strategies for serverless computing using machine learning–based auto-scaling and event-stream intelligence. It demonstrates how predictive models, real-time stream analytics, and intelligent workload profiling can proactively scale functions, reduce cold starts, and optimize execution paths. Through architectural patterns and real-world enterprise use cases, the session provides practical guidance for designing resilient, low-latency serverless platforms that meet performance, reliability, and cost objectives in modern cloud ecosystems.

Profile:

Parameswara Reddy Nangi is a Senior Hadoop and Cloud Platform Engineer with over 15 years of experience in Big Data ecosystems, distributed systems, and enterprise cloud security. His professional focus includes designing, automating, and optimizing large-scale data platforms with strong emphasis on compliance, high availability, and multi-cloud operations.

He has contributed extensively to enterprise cloud modernization initiatives, AI-driven automation frameworks, and advanced security architectures. Parameswara has also served as a judge, reviewer, and session chair for several international technical conferences and innovation forums.

In addition to his technical leadership, he has served as President of a nonprofit organization, leading multiple community, cultural, and health awareness initiatives across Texas. His combined expertise in technology, leadership, and innovation positions him as a distinguished speaker for global conferences.