November 24, 2025. Today felt like any other day in the bustling world of infrastructure ops, but little did I know it was about to be anything but mundane.

It started innocently enough—a simple request from our platform team: “Hey Brandon, can you look into why the Wasm worker on Cloudflare is misbehaving?” My initial response? “Sure thing. Let’s dig in.”

I fired up my terminal and SSH’d into one of the affected instances. A quick top revealed high CPU usage, which was unusual for a Wasm worker that was supposed to be lightweight. I decided to use strace to trace system calls, but something felt off.

“Wait,” I thought, “I’ve heard about eBPF being production-proven in recent months. Maybe that can give us more insight into what’s happening here.”

So, armed with bpftool, I dove deeper. The output was verbose and somewhat cryptic at first glance, but as I pored over the logs, a pattern began to emerge: repeated calls to /sys/module/bpf/parameters/debug.

This got me thinking—could it be an issue with eBPF itself? Or perhaps some kind of misconfiguration?

I decided to take a more aggressive approach. After ensuring backups were in place (always a good practice), I added the debug=1 parameter directly into /sys/module/bpf/parameters/debug, effectively enabling debugging mode.

To my surprise, the system call output became much clearer. It was clear that something was making frequent and unnecessary calls to the bpf module—a potential sign of an application-level issue or maybe even a misbehaving kernel module.

I spent a few hours debugging this with perf, trying to correlate these syscalls back to the Wasm worker’s code. At one point, I found myself staring at a line of assembly that seemed out of place—something about a ret instruction being called repeatedly in an unexpected context.

That’s when it clicked: the Wasm worker was somehow getting stuck in a loop, triggering unnecessary syscalls and eating up CPU resources.

I quickly crafted a small eBPF program to trace these specific syscalls, which helped me identify that the issue stemmed from a library used within the Wasm code. The library had some unhandled edge cases that were causing it to trigger additional checks more frequently than intended.

Once I identified this culprit, it was just a matter of updating the code and redeploying. After deploying the fix, the CPU usage normalized, and the system seemed stable again.

But the real learning point here is how powerful eBPF can be when you’re dealing with complex multi-cloud deployments. It’s not just about monitoring; it’s about understanding what’s happening at a lower level that tools like strace or even advanced logging might miss.

This experience also reinforced my belief in the value of leveraging Wasm for edge cases, where traditional VMs might be overkill. The lightweight nature of Wasm combined with eBPF’s powerful tracing capabilities can provide deep insights into complex systems.

As I signed off on the commit and pushed it to our staging environment, I felt a mix of relief and satisfaction. Relief that we solved this issue without downtime, and satisfaction from using cutting-edge tools like eBPF in a real-world scenario.

Here’s to more adventures with multi-cloud deployments, AI-native tooling, and the endless quest for better infrastructure!

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And so, another day ended with a bit more knowledge under my belt. Happy debugging!