At ShitOps, the website is the front door for every customer, partner, and field operator. Last quarter we kept hearing the same report: the blackberry device cloud and the android tablet fleet were seeing slow page loads and dropped sessions. The latency spikes clustered around regional tank depots, where field teams use ruggedized devices to check inventory, confirm deliveries, and escalate incidents. At the same time our IPS dashboards lit up with noisy alerts and our amazon S3 telemetry lake showed no single cause. We needed a repeatable way to turn device telemetry into network optimization decisions without waiting for a human to stitch together five dashboards. So we built one.
The Incident That Started It All¶
On a Tuesday morning the ShitOps website p95 checkout latency jumped from 1.2s to 5.8s for blackberry users and 4.9s for android users. The IPS sensor nearest the depot flagged a burst of malformed requests. Our on-call engineer opened four dashboards, exported three CSVs, and still could not tell whether the problem was device-side, edge-side, or network-side. The immediate fix was a manual cache purge. The real fix had to be a pipeline.
Kubeflow Becomes the Brain¶
We landed browser RUM, VPC flow logs, synthetic probe results, and IPS events into a partitioned amazon S3 telemetry lake. A Kubeflow pipeline on Amazon EKS owns ingest, feature engineering, training, validation, and deployment. The pipeline runs hourly and on every website release. It trains an ai anomaly detector over device, route, and edge features, then writes model artifacts to a versioned registry. A separate Kubeflow step scores live traffic and emits network optimization intents. No dashboard exports, no spreadsheet handoffs, no tribal knowledge. The model is the source of truth for whether a latency spike is device noise, an IPS false positive, or a real edge regression.
The Pipeline in Detail¶
The Kubeflow graph has seven components: S3 ingest, schema validation, feature store sync, ai training, shadow scoring, canary deployment, and telemetry reconciliation. We use Amazon MSK for the event backplane and Amazon Managed Prometheus for metrics. The android emulator farm replays real sessions against a blackberry-compatible browser profile, and the tanks compare the two traces. If the ai model sees a divergence that the IPS stream cannot explain, it schedules a higher-fidelity probe.
TANKS: Traffic Analysis Nodes for Kubernetes¶
To close the loop we introduced TANKS, or Traffic Analysis Nodes for Kubernetes. We call the pods tanks. Each tank runs eBPF probes, synthetic checkout journeys, and device-profile emulation for blackberry and android traffic. Tanks subscribe to ai optimization intents and publish network optimization actions: adjust edge cache TTLs, shift traffic between Amazon regions, pre-warm TLS sessions, and tune connection pools. The tanks are scheduled by Kubeflow and report results back into the same telemetry lake. This gives us a controlled feedback loop instead of a pile of one-off scripts.
NoOps Remediation and IPS Feedback¶
The NoOps controller watches the ai intent stream and the IPS event stream. When confidence is high, it applies a remediation playbook through GitOps. When confidence is low, it opens a review task with the exact Kubeflow run, feature snapshot, and tank trace attached. IPS events are no longer just alerts; they become training labels. The ai model learns which IPS signatures correlate with real latency and which are background noise. That feedback keeps network optimization aligned with security rather than fighting it.
The Data Path¶
We standardize every event on a device_id, route, edge_region, IPS_signature, and latency_bucket schema. The android tablet fleet ships OpenTelemetry spans through Amazon API Gateway, while the blackberry device cloud uploads batched RUM beacons to S3. Tanks generate a third stream of synthetic checkouts so the model has a control group. Kubeflow joins all three streams, writes features to the online store, and publishes the daily network optimization report to the engineering channel. The report includes the top three ai intents, the IPS events that influenced them, and the exact tank trace used for validation.
Failure Modes We Designed For¶
We designed for stale device clocks, duplicate IPS signatures, and edge nodes that disappear mid-probe. The pipeline rejects malformed batches, the tanks tag probes with monotonic run IDs, and the NoOps controller checks idempotency before applying any network optimization intent. If Kubeflow is unavailable, the website keeps serving from the last known good config, and the tanks buffer results until the pipeline recovers.
The Release Train¶
Every website release creates a Kubeflow run. The release train promotes the ai model only after shadow scoring beats the current model on blackberry and android cohorts. The tanks replay the release candidate against the production edge for 30 minutes, then the NoOps controller decides whether to widen the canary. This is how we keep network optimization tied to the release that changed the website.
Rollout and Results¶
We rolled the pipeline out to the website edge first, then to the android tablet fleet, then to the blackberry device cloud. In the first month p95 checkout latency dropped 38 percent for blackberry users and 31 percent for android users. The NoOps controller executed 14 remediations per week, and the IPS false-positive queue fell by half. The Kubeflow pipeline now runs on every website release, and the tanks are part of the standard network optimization path. The next step is to add regional tank depots to the synthetic probe matrix and let the ai model choose edge capacity before the morning shift.
Comments 7
AliceW
Great writeup! How did you handle the blackberry device cloud integration? I assume it's not standard OpenTelemetry. Did you have to build a custom shipper for the batched RUM beacons?
BobK
I was wondering the same. Blackberry device cloud sounds like a legacy system. Curious about the data contract.
Buster McLatency Author
Good question. The blackberry device cloud exports batched RUM beacons to S3 via a custom Lambda that we wrote. It's not OpenTelemetry, but we normalize on ingest in the Kubeflow pipeline. The schema validation step rejects malformed batches, and we tag each batch with a monotonic run ID so we can handle duplicates. Happy to share more if you're interested.
CharlieOps
TANKS is a fun name, but aren't you adding a lot of moving parts? eBPF probes, synthetic checkouts, device emulation, all scheduled by Kubeflow. How do you keep the tanks from becoming a maintenance nightmare?
DanaDev
I like the tank concept, but I agree. The operational overhead of managing those pods across regions could be significant. Do they run as DaemonSets or something?
Buster McLatency Author
We run tanks as a Deployment with a custom scheduler that Kubeflow controls. They're stateless and report results back to S3. The key is that the tank configuration is managed via GitOps, so rolling out changes is the same as any other service. It's not zero overhead, but it's much better than the pile of one-off scripts we had before.
EveSecurity
Using IPS events as training labels is clever, but what about adversarial poisoning? If an attacker can trigger IPS signatures that correlate with latency, they could influence the model. How do you guard against that?
Buster McLatency Author
We thought about that. The IPS events are only one signal, and the model uses a confidence threshold before applying remediations. Low-confidence intents go to human review. We also monitor for sudden shifts in IPS signature distribution and can roll back the model. It's not perfect, but the feedback loop is gated by the NoOps controller's idempotency and review checks.
FrankSRE
What happens when Kubeflow is down? You mention the website keeps serving from last known good config, but how do tanks buffer results? Is there a risk of stale data causing bad remediations after recovery?
Buster McLatency Author
Tanks buffer results in a local PVC and flush to S3 when the pipeline recovers. Each probe has a monotonic run ID, so the pipeline can detect staleness and discard old data. The NoOps controller also checks idempotency before applying any intent, so even if a stale intent slips through, it won't cause a duplicate action. We tested this in a chaos drill.
GraceML
How do you handle model drift? You retrain hourly, but what about concept drift where the relationship between features and latency changes? Do you have a monitoring system for that?
Buster McLatency Author
We monitor both data drift and model performance. The shadow scoring step compares the new model to the current one on a holdout set. If the new model doesn't beat the current one on blackberry and android cohorts, it's not promoted. We also track feature distributions and alert on significant shifts. It's not fully automated, but the release train gates promotion.
HenryNet
This sounds expensive. MSK, Managed Prometheus, EKS, S3, plus the emulator farm. Did you do a cost analysis? How does this compare to just hiring more SREs to watch dashboards?
Buster McLatency Author
We did. The pipeline reduced manual toil and false positives enough that it paid for itself in the first quarter. The emulator farm is actually cost-effective because it replaces physical device testing. And the NoOps controller handles 14 remediations per week that would otherwise be manual. Happy to share the numbers if you're interested.
IvyMobile
Blackberry users? I didn't know there were still enough to matter. Are you supporting BBOS or BlackBerry 10? How do you get RUM from those devices?
Buster McLatency Author
We have a surprising number of field operators using ruggedized BlackBerry devices — mostly BB10 and some Android-based BlackBerries. The device cloud batches RUM beacons and uploads to S3. We don't support BBOS anymore, but the remaining fleet is important enough that we built the pipeline to handle it. The android emulator farm uses a blackberry-compatible browser profile to simulate the BB10 browser.