A recent GitHub outage was caused by an Istio sidecar pod reaching its concurrency limits. The autoscaling policy for the affected service was misconfigured, as it only monitored the host service's load metrics and did not account for the Istio sidecar's limits.
Autoscaling dynamically adjusts the resources allocated to a service based on its current load. This requires defining an autoscaling policy that specifies which metrics represent load and how resources should be added or removed. Common metrics include CPU utilization, but services can become saturated even with low CPU if other factors, like I/O wait, are not monitored.
The GitHub incident demonstrates that autoscaling policies must consider all components of a service. If a policy only monitors the main service and not its dependencies, such as sidecars, it can lead to saturation and outages when those unmonitored components reach their limits. This necessitates including metrics for all critical parts of the service architecture.
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A recent GitHub outage was attributed to a misconfigured autoscaling policy on a service's Istio sidecar, which failed to scale correctly when the sidecar reached concurrency limits. This incident highlights the importance of comprehensive autoscaling metrics that account for all service components, not just the primary host service.