Case Study

Streamlined Operations with 80% Decrease in Manual Intervention in Scaling Processes

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Industry 

FinTech

Expertise 

Amazon EC2, Amazon CloudWatch, Pods

Offerings/solutions 

Enhanced scaling efficiency, cutting disruptions and costs while boosting reliability

About the Client

Credit Saison India is a prominent financial services company based in India and a subsidiary of Credit Saison Co., Ltd., a major Japanese financial institution known for its extensive experience in the credit and financial services sector. Established to cater to the Indian market, Credit Saison India focuses on providing a range of credit-related products and services.

Highlights

90%

Reduction in Service Disruptions

25%

Efficient Resource Allocation

98%

Uptime

The Challenge

The challenge involved the Horizontal Pod Autoscaler (HPA) over-provisioning during traffic spikes, launching excess pods that led to resource inefficiencies. As traffic normalized, scaling down posed risks if extra pods were on nodes set for termination by the Cluster Autoscaler, potentially removing critical application pods and causing service disruptions. Ensuring stability during these scaling operations was crucial to maintain a seamless user experience in a high-traffic production environment.

Solutions

• Introduced PDBs to ensure replacement pods run before node drainage, minimizing disruptions.
• Set minimum pod availability in PDBs to protect critical applications from termination.
• Integrated PDBs with HPA and Cluster Autoscaler for stable scaling decisions.
• Enhanced monitoring via Amazon CloudWatch for quick detection of scaling issues.
• Implemented automated rollbacks for scaling violations to ensure availability.
• Regularly adjusted PDBs to optimize scaling based on traffic changes.

The Results

Achieved a 90% reduction in service disruptions during scaling, improved resource allocation efficiency by 25%, maintained 98% application uptime, and cut infrastructure costs by 15%, all while enhancing monitoring accuracy by 95%.

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