A leading energy company experienced server loads that peaked at 2x to 10x normal levels during key business periods.
This caused significant challenges in managing their cloud infrastructure.
Task
They struggled with several issues in their cloud infrastructure setup:
Over-Provisioned Infrastructure:
Resources were designed to handle peak loads, resulting in frequent underutilization and inflated operational costs.
Inflexible Scaling in Kubernetes:
While their Kubernetes deployment featured auto-scaling for pods, the underlying host instances were oversized for maximum demand, leading to inefficiencies and wasted resources.
Static SQL Databases:
Databases were scaled for maximum load without the capability to downscale, problematic given the fluctuating nature of the business.
Action
We implemented a strategic overhaul of the firm’s cloud management:
Dynamic Resource Allocation:
Introduced dynamic resource allocation using on-demand, reserved, and spot instances for cost optimization.
Database Optimization:
Set up a primary server with read replicas for load distribution and implemented sharding for scalable, cost-effective write operations.
Enhanced Kubernetes Strategy:
Refined Kubernetes configurations by balancing on-demand nodes with spot instances for optimal load management.
Result
The strategic solutions implemented led to significant enhancements in the firm’s operational metrics:
Cost Reductions:
Overall cloud spending was reduced by 65%, with savings reaching up to 90% during off-peak periods.
Operational Efficiency:
System downtime was minimized, and resource utilization rates were significantly improved.
Scalability:
The new system adeptly managed varying loads, ensuring seamless operations across both peak and low-demand periods.
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