Debugging Cloud Cost Overruns: A Day in the Life of an Engineering Manager

August 22, 2022

It’s another sunny day at work. The air is thick with the promise of summer, and my desk is a testament to the past few months—stacks of documents, scattered laptops, and remnants of my latest code reviews. Today, however, it feels like something’s off. I can sense the presence of an unwelcome visitor in our cloud infrastructure: runaway costs.

The previous night, we were notified by Google Cloud about unexpected suspension of our production projects at 1 AM on Saturday. This happened during a critical release window! Panic was in the air as engineers scrambled to understand what went wrong and how to fix it quickly.

The Incident

To give you some context, earlier this year we had migrated all our services to Google Cloud Platform (GCP) for its robust security features and ease of use. We were using Managed Instance Groups for autoscaling, Kubernetes clusters for container orchestration, and a variety of managed databases like Firestore and Spanner. Everything seemed to be running smoothly until Saturday night.

The incident occurred during the scheduled deployment window when we pushed out a new version of our platform that included several critical bug fixes and performance enhancements. The logs were clear; everything deployed successfully. However, around 1 AM, our monitoring tools started spiking with alerts: CPU utilization on some instances was at 98%, and network egress had doubled overnight.

Debugging the Issue

I gathered a small ops team to dig into the issue right away. We started by checking the resource usage metrics in the GCP Console. The CPU spikes seemed random, but the network egress looked like a data dump. Digging deeper, we found that one of our custom-built microservices was responsible for this behavior.

The microservice in question was an internal logging aggregator that aggregated logs from various services and sent them to a third-party analytics tool. It turned out that during the deployment, a configuration change caused it to buffer all incoming logs without sending them immediately. As we continued pushing more and more logs, the buffer overflowed, causing massive network egress.

The Fix

We rolled back the deployment, fixed the configuration issue, and deployed the changes right away. After about an hour of intense monitoring, we saw that the CPU spikes had subsided, and the network egress stabilized. We then set up alerts for these metrics to ensure such issues could be caught early in the future.

But this was just the beginning. The cost overrun was real and painful. We quickly realized that GCP’s pay-as-you-go model was not our friend during this unexpected surge in traffic. We needed a better understanding of how our infrastructure would scale under different load scenarios.

Lessons Learned

1. Monitoring is Key: We need to have better real-time monitoring for critical metrics like CPU utilization and network egress.
2. Config Management: Automated testing should catch configuration changes that could impact resource usage.
3. Cost Optimization: Regularly review our cloud spend, especially during high-traffic periods.

Moving Forward

This experience has highlighted the importance of robust cost management practices in modern infrastructure. We’ve started implementing more detailed cost tracking and optimization strategies to ensure we can handle unexpected surges without breaking the bank.

As I sit here reflecting on this day, it’s clear that no matter how well-engineered our systems are, Murphy’s Law always applies. But with better monitoring, testing, and proactive management, we can mitigate these issues before they become full-blown disasters.

Until next time,

Brandon

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This isn’t just a tale of tech; it’s about the real work, the late-night debugging sessions, and the lessons learned. The tech industry moves fast, but the challenges remain the same—managing costs while ensuring reliability and performance.