Our client, a prominent clinical research organization, is committed to expediting clinical trials and bringing new treatments to patients. They emphasize patient safety and operational efficiency and are pivotal in advancing medical research and enhancing healthcare outcomes.
Our customer had substantial expenses related to non-production environments and specifically their Google Kubernetes Engine (GKE) workloads. This was not viable for the business and the customer was keen to reduce their overall cloud expenses by a minimum of 30% and tighten security. The cost optimization measures needed to maintain the functionality and high availability of the workloads.
The CloudifyOps team focused on rightsizing the resources to match the workload requirements accurately. The GKE worker nodes, the machines that run containerized applications and other workloads, were evaluated and optimized. This highlighted the need for reservation plans for the worker nodes. Using Reserved and Spot Instances was ideal for the customer and helped optimize cloud costs. Karpenter, a Kubernetes-based auto-scaling solution was implemented with GCP Node Termination Handler to ensure scalability and leverage Spot Instances effectively. A bastion host designed to withstand attacks was configured to remotely access the private Kubernetes clusters through a private IP address. Sensitive data was encrypted in use and transit.
30-50% – Estimated cost savings from choosing Reserved Instances compared to On-demand
60-80% – Average CPU and memory utilization across GKE worker nodes that will lead to a potential 10% cost reduction from rightsizing the resources
70-90% – Estimated cost savings by using Spot Instances compared to On-demand
A layered approach to protect the customer’s clusters and workloads and keep the security in check.
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