Cases2 cases
1 to 2 of 2
IL-0293Cloud Bigtable in us-central1 had higher latency and connection errors for about 32 hours from low-rate packet lossGoogle Cloud2023- Symptoms
From 19:50 US/Pacific on 4 December 2023, Cloud Bigtable customers in us-central1 saw higher latency and a low level of connection errors, affecting about 0.06% of Bigtable dataplane requests. Automated monitoring first detected the problem at 23:35.
- Trigger
A change that caused an isolation failure on the machines running Cloud Bigtable servers.
- Root cause
- Component
- Contributing factor
- –
- Detection
Automated monitoring detected the problem at 23:35 US/Pacific on 4 December 2023.
- Remediation
- Redirected some traffic away from a subset of the affected machines
- Rolled back the change that triggered the isolation failure
- Action items
- Put controls in place to prevent a recurrence
IL-0530Virtual router controllers in us-central1 ran out of memory in a loop, cutting networking for new and migrated VMsGoogle Cloud2023- Symptoms
On 5 October 2023 in us-central1, newly created VMs waited a long time before their networking worked, and VMs that had been live migrated lost connectivity for a long time after the migration. Networking telemetry showed cross-region packet loss to and from us-central1 from 03:08 US/Pacific. Health check state of VMs reached load balancers late.
- Trigger
A management plane behaviour change, meant to decouple the processing of API updates to Instance Groups and Network Endpoint Groups used as load balancer backends, was deployed in us-central1 after rolling out to several other regions without incident.
- Root cause
- Component
- Contributing factor
- Detection
Engineers were alerted to slowness in the virtual network control plane in us-central1 at 21:45 US/Pacific on 4 October, before the packet loss started, and to controller failures from memory exhaustion at 02:11 on 5 October. Networking telemetry showed cross-region packet loss from 03:08.
- Remediation
- Increased the memory allocated to the virtual network router controllers
- Rolled back the management plane change in us-central1
- Restarted by hand the routers that were still not accepting updates
- Action items
- Alert proactively on memory risks and unexpected memory increases
- Refactor the deployment configuration so engineers can reallocate memory much faster
- Re-evaluate existing practices and add safety mechanisms for API reconciliation
- Make management plane changes more visible across teams so the changes can be correlated faster
- Adjust the deployment footprint so a regional API change is less likely to exhaust memory across the region at once
- Optimise memory use in the traffic routers and their controllers
These 2 cases involve Cloud load balancer and Database. kubegap audits a Kubernetes cluster for gaps like these, read-only, from Claude Code or Codex; its checks cite public reports like the ones in this library.
By root cause1
Faulty deployment2 cases
- Contributing factor
- Organisation