Case Study

Improved Application Performance with 25% Faster Response During Traffic Spikes

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Industry 

FinTech

Expertise 

AWS Lambda, Amazon CloudWatch, Amazon EC2, AWS CodeDeploy

Offerings/solutions 

Warm pools and auto-scaling cut readiness time and optimized resource use.

About the Client

Credit Saison India is a prominent financial services company based in India. It is 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

30 Seconds

Reduced Readiness Time

80%

Automated Scaling Enhanced Uptime

20%

Dynamic Scaling Optimized Costs

The Challenge

The lack of dynamic scaling policies resulted in manual, time-consuming scaling operations that delayed responses to traffic spikes. This inefficient approach required significant intervention, and new instances averaged 4 minutes to deploy, limiting their availability during critical demand periods. Consequently, resource management suffered, leading to over-provisioning during low traffic and under-provisioning during peaks, compromising the system’s ability to meet demand effectively.

Solutions

• Integrated warm pools to keep pre-initialized instances in a stopped state.
• Reduced instance scale-out time from 4 minutes to under 30 seconds by leveraging warm pools.
• Implemented dynamic scaling policies based on multiple metrics:
• Hits to the ALB target group to track traffic surges.
• CPU usage of the Java service to prevent overloading instances.
• Memory consumption to ensure sufficient resource availability.
• Utilized AWS Lambda to automate the deployment of application code to new instances.
• Ensured new instances were immediately ready to handle traffic upon launch using AWS Lambda automation.

The Results

Implemented warm pools and automated dynamic scaling to reduce instance readiness time to under 30 seconds, increase availability by 15%, and cut costs by 20% while enhancing performance and operational agility.

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