About Me
I’m Josephine Eskaline Joyce, a STSM (Chief Architect) at IBM and an influential thought leader in cloud-native technologies, platform engineering, and cloud security. With over 24 years of experience, I’ve been driving innovation in cloud architecture, Infrastructure as Code (IaC), AI-driven automation, and resilient DevOps practices.
I’m an active technology blogger, passionate about translating complex cloud and AI concepts into practical insights. My articles on Cloud Security, Cloud Architectures, Platform Engineering, and event-driven automation have been featured in leading publications, including the DZone Enterprise Security Trend Report (2024), where I was recognized as a DZone Core Expert in Security. Currently, I’m pursuing my Ph.D. in Cloud Computing at Christ University, Bangalore, where my research focuses on AI-driven performance prediction for LLM applications. My academic work bridges research and practice through studies on Kubernetes autoscaling for AI workloads, microservices scalability, and cloud resilience. I hold patents in push notifications, cloud security, and infrastructure automation. I’m deeply committed to mentoring and community engagement, inspiring early-career professionals and students to embrace engineering excellence and innovation through a blend of technical depth, academic rigor, and inventive thinking.What I Work On
- Cloud-Native Architectures and Infrastructure as Code (IaC)
- Platform Engineering and Intelligent Automation
- Cloud Security and Zero-Trust Implementations for Enterprise Applications
- Performance Engineering and Auto-Scaling in Large Language Model (LLM) Workloads
Publications
- IEEE Paper — Designing a Multi-Layered Rate Limiting Framework for Resilient Cloud-Native Systems
- IEEE Paper — Autonomous Infrastructure Healing: A Multi-Agent Kubernetes Recovery Framework
- IEEE Paper — A Design-Driven Taxonomy of AI Agentic Patterns
- IEEE Paper — Secure by Design: Strategic Approaches to Infrastructure as Code
- Springer book chapter — Maximizing Efficiency: Unveiling the Potential of Kubernetes Metrics
- IEEE Paper — DevOps Dynamics: Tools Driving Continuous Integration and Deployment
- IEEE Paper — Enhancing Kubernetes Auto-Scaling: Leveraging Metrics for Improved Workload Performance
- IEEE Paper — Reinforcement Learning based Autoscaling for Kafka-centric Microservices in Kubernetes
Patents
- Intelligent automated feature toggle system using annotations
- Channel to report push notifications as spam
- Dynamic message embedded within application new feature rollout
- Calculating and displaying implicit popularity of products
- Synchronizing data across multiple instances of an application in a cloud
- Dynamic control of autonomic management of a data center
- Theme-based push notifications
- Intelligent distribution of push notifications
- Generating structured meeting reports through semantic correlation of unstructured voice and text data
- Automatically tuning middleware in a mobilefirst platform running in a docker container infrastructure
- Dynamic control of autonomic management of a data center