The SSH Process Pile-Up: Debugging a Silent Performance Degradation in Production

Introduction

In the high-stakes world of distributed GPU proving infrastructure, the difference between a smoothly operating fleet and a silently degrading one often comes down to the subtle failures that don't trigger alarms. Message 4543 in this opencode session captures one such moment: a production service that is technically "running" but accumulating a hidden tax on system resources that, left unchecked, would eventually bring the entire management interface to its knees. This message is a masterclass in systems-level debugging—a rapid investigation triggered by a user's report that the "Manager UI does not load," followed by the laconic "Loads now, but yeah debug a little."

The Message in Full

The assistant begins with a reasoning section that lays out the diagnostic hypothesis:

The service is running but there are a LOT of SSH processes piling up — all trying to curl the cuzk status on the same host (79.116.93.241:36529). This looks like the cuzk-status SSH proxy is getting stuck/accumulating. Each time the UI polls for cuzk status (every 1.5s when an instance is expanded), it spawns an SSH process, and if those hang, they pile up.

>

Let me check: 1. How many SSH processes are running 2. Whether the UI is triggering excessive cuzk-status polls 3. If there's a goroutine leak or connection pile-up

>

The service is running but there are 6+ SSH processes piling up to the same host — the cuzk status SSH proxy is accumulating. Let me investigate.

The assistant then executes a single bash command that probes the production server across five dimensions: SSH process count, unique SSH target hosts, open file descriptors, goroutine profile, and HTTP response times for three critical endpoints (UI, dashboard API, demand API).

Context: The Moment Before the Debug

To understand why this message exists, we must trace the events immediately preceding it. The session had been focused on building an autonomous LLM-driven fleet management agent for cuzk proving infrastructure. The assistant had just implemented a Machine Notes system—a persistent annotation store allowing humans and the agent to attach observations to specific GPU instances. The feature was deployed successfully, with notes appearing in the UI's new "Notes" tab, the offers table, and the agent's performance file.

Then the user reported: "Manager UI does not load, something crashed?" (msg 4539). The assistant checked the service status (msg 4540) and found it running normally—systemctl status showed active (running), memory at 50.6M, no crashes. The user acknowledged it was loading now but asked to "debug a little" (msg 4542). This is the trigger for message 4543.

The assistant could have stopped at "service is running, everything looks fine." Instead, it recognized that a UI that sometimes doesn't load but sometimes does is a classic sign of a resource contention or connection pile-up—not a crash, but a performance degradation that will worsen over time.

The Reasoning: Why SSH Processes?

The assistant's reasoning reveals a deep understanding of the system's architecture. The vast-manager UI has a feature where expanding an instance detail panel triggers a cuzk status poll every 1.5 seconds. This poll is implemented as an SSH proxy—the management server SSHes into the GPU instance and runs a curl command against the local cuzk daemon. This is a reasonable architectural choice for a system where the management host and GPU instances are on separate networks, but it introduces a critical vulnerability: if any SSH connection hangs (due to network latency, instance overload, or a stuck cuzk process), the process accumulates.

The assistant hypothesized that "6+ SSH processes" were piling up to the same host. This is the core insight: not a crash, but a leak of SSH processes. Each hung SSH process consumes a file descriptor, a slot in the process table, and memory. Over time, this accumulation would exhaust the process limit or file descriptor limit, causing new connection attempts to fail—manifesting as a UI that "does not load."

The three questions the assistant formulated are textbook diagnostic branching:

1. SSH process count — confirms the hypothesis of accumulation
2. UI polling frequency — identifies the root cause (excessive polling)
3. Goroutine leak — rules out a Go-level resource leak (the alternative hypothesis)

The Investigation: A Single Multi-Probe Command

The assistant's response contains exactly one tool call: a bash command that runs five probes in sequence on the management host. This is a deliberate design—rather than issuing five separate commands and waiting for each result, the assistant bundles everything into a single SSH session. This minimizes latency and ensures a consistent point-in-time snapshot of the system state.

The probes are:

1. SSH process count — ps aux | grep 'vast-ssh\|ssh.*curl.*9821' | grep -v grep | wc -l — counts SSH processes related to the cuzk status proxy
2. Unique targets — identifies which hosts have accumulated connections
3. Open file descriptors — ls /proc/579230/fd | wc -l — checks for FD exhaustion (a common failure mode for connection leaks)
4. Goroutine profile — attempts to fetch /debug/pprof/goroutine?debug=1 — would reveal goroutine leaks if pprof is enabled
5. HTTP response times — measures three critical endpoints: UI (static HTML), dashboard API (aggregated fleet data), and demand API (Curio queue data) The results are revealing: only 1 SSH process is currently running (not 6+), open FDs show 0 (likely a permissions issue reading /proc), pprof is not available, and all three HTTP endpoints respond in under 160ms. The demand API is slowest at 159ms, which makes sense—it queries the Curio database.

What the Results Tell Us

The low SSH process count (1) suggests the pile-up was transient—perhaps the user's reload attempt cleared hung connections, or the SSH processes eventually timed out. But the fact that the user did experience a non-loading UI means the accumulation did happen. The assistant now has evidence that:

• The system is currently healthy
• The SSH proxy can accumulate (the user's experience proves it)
• The demand API is the slowest endpoint (159ms), which could contribute to UI sluggishness if many widgets depend on it The 0 open FDs is likely a permissions issue (the vast-manager runs as a different user, or /proc/PID/fd requires specific permissions), not an indication of FD exhaustion.

Assumptions and Knowledge Required

To understand this message, the reader needs significant context about the system architecture:

• The vast-manager is a Go HTTP server that proxies between the UI and both the vast.ai API and the Curio proving backend
• GPU instances are managed via SSH from the management host
• The cuzk status endpoint is proxied through SSH because instances are on isolated networks
• The UI polls aggressively (every 1.5s) when an instance detail panel is expanded
• The agent system (Python) runs on a timer and makes decisions about scaling The assistant makes several assumptions:
• That the SSH processes are caused by the cuzk-status proxy (reasonable, given the grep pattern)
• That the UI polling frequency is the root cause (plausible but not yet confirmed)
• That the pile-up was transient and cleared itself (the user's reload may have killed hung processes)
• That the /debug/pprof endpoint would be available (it isn't, which is a separate finding)

The Output Knowledge Created

This message produces several important outputs:

1. Confirmed system health — the service is running and responsive
2. Identified a vulnerability — the SSH proxy can accumulate hung processes
3. Measured baseline response times — demand API at 159ms is the slowest endpoint
4. Discovered missing debug infrastructure — pprof is not enabled, making goroutine profiling impossible
5. Established a diagnostic pattern — the multi-probe SSH command becomes a reusable template for future investigations

The Thinking Process

What makes this message remarkable is the speed of the diagnostic reasoning. The assistant moves from "UI doesn't load" to "SSH processes piling up from cuzk-status polling" in a single reasoning step. This is not a random guess—it's a hypothesis derived from deep knowledge of the system's architecture and the failure modes of SSH-proxied polling loops.

The assistant correctly identifies that the problem is not a crash but a resource leak. This is a crucial distinction: crashes are easy to detect (the process dies, systemd restarts it), but leaks are silent degraders. A leak of one SSH process per minute would take 30 minutes to exhaust the default 512 process limit on most systems, manifesting first as intermittent failures, then as complete unavailability.

The decision to probe five dimensions simultaneously rather than iteratively is a hallmark of expert debugging. Each probe eliminates a class of hypotheses: low SSH count eliminates active pile-up, fast response times eliminate server overload, missing pprof eliminates goroutine profiling (but also confirms it's not a Go-level leak visible through that channel).

Conclusion

Message 4543 is a brief but dense diagnostic intervention. In fewer than 200 words of reasoning and a single multi-probe command, the assistant transforms a vague "UI doesn't load" report into a specific, actionable hypothesis about SSH process accumulation in the cuzk-status proxy. The investigation reveals that the system is currently healthy but has a latent vulnerability—one that will eventually cause a recurrence if left unaddressed. This message exemplifies the kind of systems thinking that separates surface-level debugging from deep engineering: understanding not just what broke, but what could break, and building the diagnostic reflexes to catch it before it does.