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April 5, 2026 • 6 min read

Performance Engineering at Scale: Replacing 47 Sequential lsof Calls with a Single ss Snapshot

My ecosystem health check script was taking 14 seconds to run. That's 14 seconds of wall-clock time every time I wanted to know whether all 47 services were up across my 23-port stack — COMET on 5173, MuXD on 5174, FORGE on 5175, VAULT on 5179, CIPHER on 5185, and so on down the list. When you're running 26 autonomous agents on a battle rhythm with monitoring cycles measured in minutes, a 14-second status poll isn't a minor annoyance — it's a bottleneck that cascades into stale health signals and degraded orchestration decisions.

The fix took about 40 minutes to implement and brought the runtime down to under 400 milliseconds. Here's exactly what I changed and why it matters architecturally.

The Original Architecture: One Process Per Port

The original status.sh script was written the way most port-checking scripts get written: incrementally. Each new service got appended with a quick lsof -ti :PORT call to check if anything was listening. It works. It's readable. And it scales terribly.

The core problem is that lsof is expensive. Each invocation has to open and traverse /proc, enumerate file descriptors across every running process, and filter down to network sockets matching a specific port. On a system running dual Tesla P40s with active inference workers, Ollama model shards, Docker containers, and 440GB of DDR4 in active use, the process table isn't small. Each lsof -ti :5173 call was taking 280–350ms. Multiply that by 47 services and you get the 14-second wall time I was seeing.

The script looked roughly like this for every service in the stack:

check_service() {
  local name=$1
  local port=$2
  local pid=$(lsof -ti :$port 2>/dev/null)
  if [ -n "$pid" ]; then
    echo "✓ $name (PID $pid)"
  else
    echo "✗ $name DOWN"
  fi
}

check_service "COMET"    5173
check_service "MuXD"     5174
check_service "FORGE"    5175
check_service "WEBMASTER" 5184
check_service "CIPHER"   5185
# ... 42 more lines

Sequential, synchronous, and expensive at every step. The instinct when you see this pattern is to parallelize — run all 47 checks in background subshells and wait on them. I tried that first. It brought wall time down to about 2.1 seconds by running the lsof calls concurrently, but it also spiked CPU to 340% during the check window and introduced race conditions in the output ordering. Not acceptable for a monitoring loop that feeds into agent decision-making.

The Right Mental Model: Snapshot, Don't Poll

The insight that unlocked the real fix came from thinking about this differently. I didn't need to ask the OS 47 separate questions. I needed one answer — a complete picture of what's listening on the network — and then query that picture 47 times in memory.

That's exactly what ss does. The ss utility (socket statistics, part of iproute2) can dump the entire kernel socket table in a single syscall. Instead of traversing /proc for every check, you get one atomic snapshot of all listening ports in under 10ms, then do your matching in pure shell string operations.

# Single snapshot — runs once, ~8ms
LISTENING_PORTS=$(ss -tlnp 2>/dev/null | awk 'NR>1 {print $4}' | grep -oP ':\K[0-9]+' | sort -n)

check_service() {
  local name=$1
  local port=$2
  if echo "$LISTENING_PORTS" | grep -qx "$port"; then
    echo "✓ $name"
  else
    echo "✗ $name DOWN"
    SERVICES_DOWN=$((SERVICES_DOWN + 1))
  fi
}

# Same 47 calls — now each is a grep against a string, not a syscall
check_service "COMET"    5173
check_service "MuXD"     5174
check_service "FORGE"    5175
# ...

Runtime: 380ms total. That's a 97.3% reduction. More importantly, the CPU profile is completely different — one brief spike for the ss call, then near-zero for 47 in-memory string comparisons.

Why This Matters for Agent Orchestration

The performance win is obvious, but the architectural implication is what I actually care about. In the ARKONA ecosystem, status.sh isn't just a human-facing diagnostic tool. It's called by the inter-agent communication broker before routing tasks, by the MuXD router when deciding whether to fall back from a local Ollama model to Claude cloud, and by several of the 26 autonomous agents on their pre-task health checks.

Under the old implementation, a monitoring agent running on a 3-minute battle rhythm would spend nearly 8% of its cycle time just waiting on port checks. That's 8% of its operational window consumed by I/O overhead with zero analytical value. From an agent efficiency standpoint, this is the kind of waste that compounds — if 10 agents each do a status check before executing, you're burning 140 seconds of aggregate wall time per cycle on lsof calls alone.

This connects directly to something I think about a lot in multi-agent system design: the overhead budget. Every agent in a mesh has a fixed operational window, and every syscall, network round-trip, and blocking I/O operation is a debit against that budget. NIST SP 800-160 Vol. 2 frames this as "mission effectiveness" — the ratio of time a system spends doing mission-relevant work versus overhead. Sloppy instrumentation that looks harmless at small scale becomes a systemic drag when you multiply it across a fleet of autonomous actors.

The Broader Pattern: Batch Your Observations

The lsof-to-ss refactor is one instance of a pattern I now look for everywhere in the ecosystem: replace repeated fine-grained observations with a single coarse-grained snapshot.

I applied the same logic to GPU thermal monitoring. The original approach polled nvidia-smi separately for each of the two P40s to check temperature against my 70°C/75°C/80°C threshold ladder. Each nvidia-smi invocation takes ~180ms. Replacing that with a single nvidia-smi --query-gpu=index,temperature.gpu --format=csv,noheader call that returns both GPUs in one pass cut the thermal check from ~360ms to ~185ms and eliminated the time window where GPU0 had been checked but GPU1 hadn't — a subtle consistency issue that could theoretically cause a split thermal decision.

The same pattern applies to log scraping. Instead of running 12 separate grep passes over rotating log files to check for error conditions in each service, a single grep -r with a compound pattern against the log directory produces one unified result set. The matching then happens against an in-memory array rather than spawning 12 child processes.

Measuring the Cumulative Impact

After rolling out these changes across all monitoring scripts, I instrumented a 24-hour sample of agent health-check cycles. The numbers were telling:

With 21 of 22 primary services currently online and 240 commits in the last 7 days, the monitoring layer is under real continuous load. Shaving 97% off each check cycle meaningfully changes what's possible in terms of monitoring resolution — I can run health checks every 30 seconds instead of every 3 minutes without introducing any meaningful overhead.

Key Takeaway

When you're debugging performance in a multi-agent system, the first question shouldn't be "how do I parallelize this?" It should be "why am I making this call at all, and can one call replace many?" Parallelism manages latency; eliminating redundant work manages cost. In an ecosystem where autonomous agents are making hundreds of observability calls per hour, the difference between a 14-second sequential scan and a 380-millisecond snapshot isn't just a faster script — it's the difference between a monitoring layer that's always slightly behind and one that gives your agents a genuinely current picture of the world they're operating in.

The underlying principle scales far beyond shell scripts: in distributed systems, prefer bulk reads over point queries, prefer snapshots over polling, and always measure the overhead budget your infrastructure consumes before optimizing the workloads running on top of it.

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