Sravan Akinapally
American Airlines
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Cloud Enthusiast , specializing in Infrastructure as Code (IaC), Infrastructure Automation, and Azure. Experienced in managing and optimizing Kubernetes clusters for scalability, resilience, and cost efficiency. Passionate about platform engineering to enable faster time to market for developers building applications, leveraging Kubernetes for orchestration, containerization, and microservices architecture.
Area of Expertise
Supercharge Your Kubernetes Autoscaling with Custom Metrics
Out-of-the-box, Kubernetes provides native horizontal scaling capabilities driven by conventional resource consumption signals like CPU and memory utilization. However, in the real world, numerous applications demand dynamic scaling orchestrated by custom business telemetry such as queue depths, throughput volumes, or other domain-specific indicators.
This session will unravel the secrets of extending Kubernetes' Horizontal Pod Autoscaler (HPA) to leverage custom metrics as scaling triggers, unlocking unprecedented scaling autonomy. Attendees will witness live demos showcasing:
Deploying a custom metrics provider to expose application-centric metrics to the Kubernetes control plane
Configuring the HPA to consume these custom metrics for intelligent scaling decisions
A sample application dynamically scaling based on a custom metric like queue length or requests per second
Best practices for crafting bespoke scaling policies tailored to custom metrics.
Reducing Cloud Cost for Multi-Tenancy Kubernetes Platform
A self-service multi-tenancy Kubernetes platform offers many benefits to application teams. In less than 2 years, American Airlines Shared K8 Platform has grown to over 1000+ deployments. Now that we built a resilient and secure platform, we must make it cost-effective to ensure long-term viability. This has the added benefit of reducing the carbon footprint of our platform.
In the 2nd year, our platform grew by over 300% but costs increased by 500% as we added security, observability, and other features. How do we start to control costs without violating our self-service model? What is the reasonable amount to spend on Observability? What is a reasonable utilization goal and how do we get there? What level of cost optimization can we achieve without compromising our self-service model and maintaining the resiliency of our platform? We set out to address all these questions and this is our journey.
In 4 months, we decreased the total Cost Per Utilized Core (CPUC) by 40%.
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