A Sustainable Approach to Processing Big Healthcare Data (BHD) Workloads
The growing volume of Big Healthcare Data (BHD) workloads requires sustainable solutions to minimise environmental impact. This poster presents a pilot architectural framework that leverages Microservices Architecture (MSA) to enhance the sustainability of BHD workflows. The framework optimises cloud scaling strategies by integrating energy-efficient models with autoscaling tools, such as Kubernetes Event-driven Autoscaling (KEDA) and Horizontal Pod Autoscaling (HPA).
Authors
- T. S. Haddad (ORCID: https://orcid.org/0009-0009-5187-1168)
- Pushpa Kumarapeli (ORCID: https://orcid.org/0000-0002-9552-6835)
Institutions
- Kingston University (US)
Publication Details
- Journal
- Studies in health technology and informatics
- Published
- 2025-05-15
- DOI
- https://doi.org/10.3233/shti250463
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00