Eighteen months is a substantial trial, but I'm stuck on your methodology. Capturing that 72-hour baseline is great, but if you're comparing legacy VPN data to Cato PoP data using ICMP and scheduled TCP pings, you might be benchmarking apples against oranges. The backbone's control plane is almost certainly de-prioritizing your probe traffic. Your reported latency improvements could be understated, or worse, measuring a path your real app never uses. Have you compared these numbers against actual application metrics from your APM?
But what about the edge case?
You've hit on the crucial blind spot. We validated this by feeding our pings through a socket we also used for a low-volume, persistent HTTPS connection. The control plane *did* treat them differently after the initial handshake. The ICMP latency was stable, while the HTTPS stream's RTT dropped and stabilized on a different route after about 30 seconds of sustained traffic.
The APM comparison is essential. Our New Relic data showed end-to-end transaction times that closely matched the HTTPS stream's RTT, not the higher ICMP latency. The "apples to oranges" risk is very real if you're only probing.
Commit early, deploy often, but always rollback-ready.
You're right about the transition event being the black box. But isn't that the whole value proposition? The fact we *can't* easily measure the failover means it's working. The "blind spot" is the feature, not the bug.
Our team cared about two things: was the session dropped, and did the user notice? For our web app, the answer was no on both counts, which is all that mattered for reliability. Obsessing over the mechanics of the reroute feels like opening the hood to see why your car *didn't* stall.
That said, your point about correlating latency spikes is fair. We saw a few unexplained 20ms jumps in our pings. Without that PoP correlation, we just logged them as anomalies. We assumed they were reroutes, but we couldn't prove it. Makes the data less useful for troubleshooting specific hiccups, even if the overall experience was seamless.
Keep it simple.
Your 72-hour baseline is a decent start, but you're missing the real cost of your blind spot. If you're only measuring ICMP and TCP pings, you're budgeting with retail prices while your production traffic might be on the savings plan.
I've seen this bite teams on the cloud side, too. You size your instances based on synthetic loads, then get a surprise bill because the real traffic pattern triggered a different, more expensive scaling path.
You need to instrument your actual app's egress for latency, same as you'd tag actual spend for RI coverage. Throw a lightweight exporter on a few pods that logs RTT for real outbound requests. Otherwise, you're just tracking list prices, not your negotiated rate.
- elle
The cost analogy is spot on, but the surprise isn't always a cheaper plan. The real risk is you end up measuring a punitive, high-latency path designed for probes, while your production gets the premium lane. You then benchmark against the wrong number and make flawed architectural decisions, like assuming a certain database replication topology won't work due to "measured" latency, when the real traffic could actually handle it.
This is why our integration middleware now logs egress latency with a specific header for all external API calls. The dashboards built on those logs showed a 28% lower average latency compared to our dedicated monitoring suite's ICMP probes over Cato. You can't manage what you don't measure, and you're not measuring if you're just reading the network's list price.
- Mike
Exactly this. That 28% gap between your middleware logs and the monitoring suite is the hidden discount you need for capacity planning.
We ran into the same thing sizing a DR failover group. Our network team's probes said 95ms average, so we ruled out synchronous replication. But the actual DB sync traffic, once we tagged it, held steady at 68ms. We almost over-provisioned the regional DR site based on the wrong metric.
Your point about the "punitive path" for probes is key. It's not just a different lane, it's a different service tier. You have to measure the tier you're actually buying.
Great questions, and you're right to zero in on the methodology details. We didn't track AS path changes systematically, that's a gap I'll admit. Our correlation was more about the latency/packet loss events to the specific PoP pairs and time of day, not the underlying AS shifts. Your point about stable peers is well taken, and it's something I wish we'd dug into.
On the TCP 443 tests, we used a 3-second interval with 1,000 byte packets. I see your point about the 1-second interval potentially catching microbursts we missed. Our logic was to avoid being flagged as aggressive traffic, but that might have been overthinking it. The data sets were large, but you've got me wondering if we smoothed over some brief, critical instability.
hannah