为何多数资源管理器（Yarn、Mesos、Kubernetes等）不支持容器磁盘I/O资源用量设置？

Great question! This is a common pain point for developers and ops folks who need finer-grained control over container resources. Let’s dive into the technical reasons behind this gap:

• Kernel-Level Complexity & Inconsistency
  CPU and memory limits rely on mature, well-integrated Linux cgroup subsystems (cpu/cpuacct and memory) that work consistently across workloads and hardware. Disk I/O control, however, uses the blkio cgroup subsystem, which is far more finicky. Different storage devices (HDDs, SSDs, distributed storage) have wildly different I/O behaviors—for example, SSDs leverage parallelism that makes IOPS/bandwidth limits hard to enforce accurately. Older schedulers like CFQ (which blkio relied on) have been replaced by deadline/noop schedulers, which don’t support the same level of per-process I/O throttling, leading to inconsistent results.

• Container Storage Abstraction Blurs Boundaries
  Containers use a mix of storage types: bind mounts, persistent volumes, overlay filesystems, and network storage (like NFS). Unlike CPU/memory, which are isolated to individual container processes, disk I/O is tied to the underlying storage device or filesystem. If you limit a container’s I/O on a bind mount, you might accidentally throttle other applications using the same disk. For network storage, the storage system itself often has its own I/O controls, making container-level limits redundant or ineffective. This ambiguity makes it hard to design a universal, safe I/O limiting mechanism.

• Lack of Standardized, User-Friendly Interfaces
  Tools like Docker and Kubernetes added disk I/O controls much later than CPU/memory limits. While you can use flags like docker run --device-read-bps /dev/sda:100mb or Kubernetes’ resources.limits.ephemeral-storage, these are often hidden in advanced settings. Most resource managers prioritize UI/UX for common tasks, and since disk I/O limits are less frequently used (and more complex to configure), they don’t get the same front-and-center treatment. There’s no universal standard for how to expose these controls across tools either.

• Performance Overhead & Measurement Challenges
  Tracking and enforcing disk I/O limits adds non-trivial overhead. Unlike CPU/memory metrics, which are easy to sample at high frequency, I/O operations are slower and harder to attribute to individual containers accurately. Throttling I/O can also cause unpredictable performance spikes or latency, since disk operations are already a common bottleneck. This makes it risky to add I/O limiting to default resource manager interfaces without overwhelming users with technical details.

It’s worth noting that some modern tools do support disk I/O limits now, but they’re still not as ubiquitous as CPU/memory controls due to these underlying technical hurdles.

内容的提问来源于stack exchange，提问作者김민우