Article Per Year (5 Year)
p-Index From 2021 - 2026
0.751
P-Index
This Author published in this journals
All Journal The Eastasouth Journal of Information System and Computer Science
Tathagata Roy
Independent Researcher, Colorado, USA
Computer Science & IT

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Decoupling the Core: A Technical Roadmap for Modernizing Mainframe into Cloud-Native Microservices on Azure Kubernetes Service Tathagata Roy
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 01 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i01.1012

Abstract

Modernizing tightly coupled mainframe ecosystems built on COBOL business logic, CICS transaction flows, CA7 batch schedules, and DB2/VSAM data structures remains a technically complex undertaking for enterprises seeking cloud‑native agility. This paper presents a structured roadmap for decomposing monolithic mainframe workloads into Spring Boot microservices deployed on Azure Kubernetes Service, supported by Kafka for asynchronous, event‑driven communication. The work examines the translation of COBOL Copybooks into JSON or Avro schemas for API‑driven interoperability, the extraction of COBOL and stored‑procedure logic into Spring Data and service layers, and the re‑engineering of JCL utilities into containerized batch pipelines orchestrated through Kubernetes‑native schedulers. It also evaluates approaches for transforming 3270 CICS interfaces into modern React or Spring Thymeleaf frontends, migrating IBM MQ patterns into Kafka‑aligned messaging, and integrating RACF‑based security models with cloud identity providers. Automated code‑analysis and refactoring tools available at the time of writing are assessed for their ability to accelerate large‑scale modernization while preserving transactional integrity and regulatory compliance. The resulting roadmap provides a technically grounded strategy for decoupling mainframe cores and transitioning toward resilient, modular, and cloud‑aligned architectures.
Architectural Decomposition Strategies for Migrating Legacy Java EE Monoliths to Cloud‑Native Microservices Tathagata Roy
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i02.1269

Abstract

This paper presents a structured technical roadmap for decomposing legacy Java EE monoliths into independently deployable, cloud native microservices. The modernization effort focuses on enterprise applications built on JSPs, Servlets, Struts 2.0, EJBs, MDBs, Axis based SOAP services, and iBATIS driven persistence layers. These systems typically rely on tightly coupled modules, shared state, and synchronous communication patterns that limit scalability and operational resilience. The proposed approach outlines the architectural refactoring required to migrate these components into a Kubernetes containerized environment. Objectives include the replacement of point to point and JTA-based two-phase commit workflows, the transformation of MDB based asynchronous logic into Kafka consumer services, the evolution of Axis SOAP endpoints into RESTful interfaces, and the transition from shared schema iBATIS implementations to decentralized, database-per-service designs. The resulting framework provides a repeatable methodology for reducing architectural debt, improving throughput, and supporting compliant processing in regulated domains such as financial services.
Observability for Java Microservices: Metrics, Tracing, and Logging in Cloud‑Native Systems Tathagata Roy
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i02.1285

Abstract

Distributed Java microservices require comprehensive observability to maintain reliability, diagnose performance degradation, and support operational continuity across diverse cloud native and hybrid environments. This paper presents a unified framework for monitoring Spring Boot based microservices through metrics instrumentation, distributed tracing, structured log aggregation, and infrastructure level telemetry. The study identifies essential JVM, application, and platform indicators required for effective performance analysis, including garbage collection behavior, thread pool utilization, connection pool saturation, request latency, and error propagation patterns. Techniques for instrumenting Spring Boot applications using Micrometer, OpenTelemetry, and structured logging are examined, along with strategies for exporting telemetry to platforms such as Dynatrace, Prometheus, Datadog, New Relic, Splunk, and Elastic Stack. The framework extends to asynchronous processing pipelines by outlining critical Kafka producer, consumer, broker, and topic level metrics such as consumer lag, partition health, request latency, and under replicated partitions that enable early detection of backpressure and message flow disruptions. Infrastructure level signals, including container resource limits, node pressure, CPU throttling, pod restarts, and autoscaling activity, are incorporated to support SRE driven diagnostics and correlation. The paper defines recommended dashboards, alert thresholds, and telemetry correlation models that reduce mean time to resolution and provide deep visibility into Java microservices operating at scale.
Performance Optimization for Spring Boot Microservices: Adaptive Memory, Concurrency, and Resource Management Tathagata Roy
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 01 (2023): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i01.1286

Abstract

Containerized Spring Boot microservices frequently encounter performance limitations arising from inefficient JVM memory allocation, blocking concurrency models, and suboptimal resource configuration. This paper presents a comprehensive analysis of optimization strategies for Java‑based microservices running on Spring Boot and Java 17, emphasizing adaptive memory management, thread‑pool tuning, and reactive resource handling. The study examines dynamic heap‑sizing techniques that align JVM memory parameters with container constraints to reduce garbage‑collection frequency and prevent allocation failures. Off‑heap memory approaches, including Netty and native allocation, are evaluated for their ability to lower heap pressure in high‑throughput I/O workloads. Concurrency optimization is addressed through queuing‑theory‑based sizing of fixed and cached thread pools to minimize contention and improve throughput in traditional blocking execution models. The analysis extends to data‑access layers, where reactive database connectivity (R2DBC) and dynamic connection‑pool tuning algorithms are applied to reduce thread waiting time and tail latency. Additional improvements include JPA query and pooling optimizations, Tomcat connector configuration, Spring Boot tuning practices, and Docker image refinements that reduce startup time and memory footprint. The combined evaluation provides a unified framework for enhancing responsiveness, scalability, and resource efficiency in cloud‑native Spring Boot microservices.
Co-Authors