How to Pinpoint and Resolve Kernel‑Level Latency in Cloud‑Native Kubernetes Clusters

Background

In cloud‑native scenarios, many clusters adopt resource oversubscription and mixed deployment to maximize utilization, but this increases competition for resources between the host and containerized applications.

When resources are scarce, kernel‑level delays such as CPU latency and Memory Reclaim Latency propagate to the application layer, causing response‑time jitter and even service interruptions for latency‑sensitive workloads.

Because observable data is often lacking, engineers struggle to correlate application jitter with system‑level delays. This article combines practical cases to show how to use the ack‑sysom‑monitor Exporter in Kubernetes for visualizing and locating kernel delays, enabling rapid root‑cause identification and mitigation of jitter caused by latency.

Memory Allocation Delay

Processes entering the slow path of memory allocation are a major source of business latency jitter. When a process requests memory and the system or container memory reaches the low watermark, the kernel triggers asynchronous reclamation (kswapd). If free memory falls below the minimum watermark, direct memory reclaim and direct memory compact occur, which can block the process for extended periods.

Direct memory reclaim : the process is blocked waiting for synchronous reclamation because memory is scarce.

Direct memory compact : the process is blocked waiting for the kernel to defragment memory into a contiguous region.

Both actions increase CPU usage, raise system load, and cause latency jitter for business processes.

CPU Delay

CPU delay is the interval from a task becoming runnable to being actually scheduled by the OS. Prolonged CPU delay can affect business, e.g., network packets arriving but the process not being scheduled promptly, leading to network latency.

Common Delay Scenarios

CASE1 : Container memory pressure – when a container reaches its memory limit, direct memory reclaim and compact block the application.

CASE2 : Host memory pressure – even with ample container memory, low host memory triggers direct reclaim for container processes.

CASE3 : Long ready‑queue wait – processes wait in the run queue due to a long queue or blocked CPU, causing jitter.

CASE4 : Prolonged interrupt blocking – heavy interrupt handling occupies the CPU, preventing timely scheduling.

CASE5 : Kernel path lock – long‑running kernel paths hold spin locks, preventing soft‑IRQ scheduling and causing network jitter.

Identifying and Solving System Delays

The ACK and OS teams provide the SysOM (System Observer Monitoring) kernel‑level container monitoring feature, exclusive to Alibaba Cloud. By viewing SysOM dashboards at the container and pod dimensions, you can observe node and container jitter.

Metric Analysis

Memory Others : The pgscan_direct line shows the number of pages scanned during direct memory reclaim. A non‑zero value indicates reclaim activity.

Memory Direct Reclaim Latency : Shows the increment in reclaim events for various latency buckets (e.g., 1‑10 ms, 10‑100 ms) when container memory hits its limit or node free memory falls below the low watermark.

Memory Compact Latency : Indicates increments in compact events caused by excessive node memory fragmentation.

CPU Delay Monitoring : The CPU Delay metric measures the time from runnable to scheduled. The WaitOnRunq Delay dashboard shows average wait time in the run queue; spikes above 50 ms suggest serious scheduling delay.

Sched Delay Count : Counts intervals where the scheduler makes no progress (e.g., 100 ms gaps). Sharp increases reveal long periods without scheduling, impacting business processes.

Problem‑Solving Steps

Address memory pressure by using the Node/Pod Memory Panorama Analysis feature to break down memory usage per pod (cache, inactive file, inactive anon, dirty memory, etc.) and identify memory “black holes”.

Apply the Koordinator QoS fine‑grained scheduling to adjust container memory watermarks and trigger early asynchronous reclamation, reducing the impact of direct reclaim.

CPU Delay Monitoring

Use the SysOM dashboards for CPU Delay and System CPU and Schedule to observe CPU scheduling latency and related metrics.

Case Study – Fast Identification of CPU‑Induced Network Jitter

Background : A financial client on ACK experienced frequent Redis connection failures. Initial network checks pointed to kernel packet‑processing delay (>500 ms) on two nodes.

Diagnosis Steps :

Inspect the Sched Delay Count dashboard and notice multiple >1 ms sched delays, indicating the CPU was not being scheduled, likely causing soft‑IRQ starvation.

Check the OS console’s node anomaly details, revealing scheduling jitter and cgroup leak anomalies.

Review the OS console’s scheduling jitter diagnostic report, confirming the issue.

Correlate the jitter with a memory cgroup leak: the kernel’s memory.numa_stat traversal in Alinux 2 caused scheduling delays.

Identify the root cause as a cron‑job that repeatedly launched containers to read logs, creating new memory cgroups whose page cache was not released, leaving zombie cgroups.

Resolution :

Temporary fix: run echo 3 > /proc/sys/vm/drop_caches to drop page cache and allow zombie cgroups to be cleared.

Enable the Alinux zombie‑cgroup reclamation feature (see reference [5]) to automatically recover leaked memory cgroups.

References

[1] SysOM kernel‑level container monitoring

[2] OS console memory panorama analysis

[3] Container memory QoS

[4] OS console scheduling jitter diagnosis

[5] Dragonfly OS resource isolation guide