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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.
Abstract:
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).
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