Conference Papers

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    Paradigm Shift from Monolithic to Microservices
    (Institute of Electrical and Electronics Engineers Inc., 2023) Saxena, D.; Bhowmik, B.
    Microservices have been making waves among forward-Thinking application development organizations. In the realm of software development, software architecture holds paramount importance because it serves as a guiding force to shape the entire life cycle of a software system. Software architecture is a foundation for complex digital components built upon a software system. Within this domain, two prevalent paradigms, monolithic and service-oriented architecture (SOA), stand distinct. While monolithic simplifies development using its integrated structure, SOA reduces complexity through modular services. However, both paradigms suffer severe scalability, development cycle, and flexibility challenges. Subsequently, microservice architecture as a modern paradigm emerges to overcome these challenges. This paper presents an in-depth analysis of the paradigm shift from monolithic to microservice architecture. It begins with exploring the monolithic and SOA conceptual landscape and their pros and cons. After that, we delve into the microservice platform, including its basic architecture and implementation stages. Furthermore, we provide the trend of the paradigm shift that highlights the recent developments in the field and identifies the research challenges associated with it. Thus, the paper brings multiple research dimensions for the researchers and lets the software and application development teams improve resilience and expedite their time to market. © 2023 IEEE.
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    Ways of Balancing Load in Microservice Architecture
    (Springer Science and Business Media Deutschland GmbH, 2024) Saxena, D.; Bhowmik, B.
    Microservices architecture has emerged as a modern paradigm to overcome challenges associated with monolithic architecture, such as scalability, deployment, and flexibility. Microservice architecture is a relatively new approach in comparison with other paradigms. It has immense potential to enhance deployment, design, container orchestration, and expansion across different computing environments, such as cloud and edge. One of the most essential features of microservice architecture is its ability to handle scaling and load balancing. The load balancer works with a scaler to distribute load efficiently across multiple instances. This paper explains the basics of load balancing, including static and dynamic algorithms, their applications, and limitations. It emphasizes the crucial role of load balancing in popular microservices orchestrators like Kubernetes, Docker Swarm, and Spring Cloud. Furthermore, we examined the existing state of the art and identified limitations associated with load balancing in microservice architecture. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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    Prediction of Remaining Useful Life in MEMS Devices Using a Stacking Model Approach
    (Institute of Electrical and Electronics Engineers Inc., 2025) Sanjay, M.; Kumar, R.S.
    Accurate estimation of the Remaining Useful Life (RUL) of Micro-Electro-Mechanical Systems (MEMS) devices is essential for enhancing predictive maintenance in industrial settings. This paper proposes a robust stacking ensemble model for Remaining Useful Life (RUL) prediction of MEMS devices, integrating sensor and mechanical component data. Publicly available RUL datasets were augmented using Generative Adversarial Networks (GANs), ensuring diverse input data. Key algorithms-Bayesian Ridge, Random Forest, and XGBoost-were identified using Lazy Predict and combined with a Support Vector Regressor (SVR) as the meta-learner. Hyperparameter optimization was performed using the Deep Deterministic Policy Gradient (DDPG) algorithm, offering enhanced efficiency for large datasets and dynamic retraining. The model demonstrated superior performance based on RMSE and R2 metrics compared to traditional methods. Deployment was achieved via a Flask web application integrated with a CI/CD pipeline using GitHub Actions and Docker, ensuring scalability and reproducibility. This research contributes a scalable and efficient framework for advancing predictive maintenance in industrial automation. © 2025 IEEE.