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A Sustainable Approach to Processing Big Healthcare Data (BHD) Workloads.
Tia Haddad1, Pushpa Kumarapeli1
1School of Computer Science and Mathematics, Kingston University London.
Studies in Health Technology and Informatics
|May 17, 2025
Summary
Sustainable Big Healthcare Data (BHD) processing is crucial. This study introduces an architectural framework using Microservices Architecture (MSA) to optimize cloud scaling for reduced environmental impact.
Area of Science:
- Computer Science
- Environmental Science
- Healthcare Informatics
Background:
- The increasing volume of Big Healthcare Data (BHD) presents significant environmental challenges.
- Sustainable solutions are needed to minimize the ecological footprint of BHD workloads.
Purpose of the Study:
- To present a pilot architectural framework for enhancing the sustainability of BHD workflows.
- To optimize cloud scaling strategies for energy efficiency in healthcare data processing.
Main Methods:
- Leveraging Microservices Architecture (MSA) for BHD workflows.
- Integrating energy-efficient models with autoscaling tools.
- Utilizing Kubernetes Event-driven Autoscaling (KEDA) and Horizontal Pod Autoscaling (HPA).
Main Results:
- A pilot architectural framework was developed.
- The framework demonstrates potential for optimizing cloud resource utilization.
- Integration of autoscaling tools with energy-efficient models was explored.
Conclusions:
- The proposed MSA framework offers a pathway to more sustainable BHD processing.
- Optimized cloud scaling can significantly reduce the environmental impact of healthcare data workloads.
- Further research is warranted to validate the framework's scalability and long-term effectiveness.
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