Analyzing Ephemeral Storage IOPS Bottlenecks with eBPF and Kernel Tracing

The invisible challenge of ephemeral storage in high-density environments

When running modern container-based workloads, ephemeral storage is often treated as a secondary infrastructure detail. In practice, this means temporary files, runtime logs, and volatile write layers share complex data paths that can choke the performance of entire applications. IOPS, which measures the number of read and write operations a disk can perform per second, becomes the primary limiter when hardware resource contention increases dramatically on a single node.

For those outside systems engineering, imagining the disk as a single-file queue helps explain the problem. When hundreds of containers attempt to write data simultaneously, a massive congestion point forms at the file system entrance. Traditional monitoring tools often fail to pinpoint the exact origin of this delay because they calculate only general averages, masking peak moments when the system stalls while waiting for a response from the underlying hardware.

Understanding kernel tracing without code modifications

Kernel tracing represents a revolution in how we investigate the internal behavior of operating systems. Historically, to understand what happened inside the Linux kernel, engineers had to modify the source code or install complex, unstable modules that frequently caused system crashes. In practice, eBPF technology changed this landscape by allowing safe programs to run directly inside the kernel space in an isolated and controlled manner.

The acronym eBPF stands for Extended Berkeley Packet Filter, a technology that started focused on network packet filtering and evolved into a universal code execution engine at the heart of the operating system. In practice, it works as a set of small probes that we can attach to strategic kernel points, known as tracepoints or kprobes. When a disk event occurs, the eBPF code intercepts this system call transparently, gathers granular metrics, and sends consolidated data to user space with negligible overhead.

Mapping the path of I/O requests within the block subsystem

To diagnose an IOPS bottleneck with surgical precision, we must track the complete journey of a read or write request. The process begins when the application requests a data write through a generic system call, such as the write function. From there, the Linux kernel translates this request into logical blocks, queues the request in the block subsystem, and finally dispatches it to the physical or virtualized device driver.

Each of these steps adds crucial microseconds to the total response time. If the ephemeral volume is mapped over a layered file system, such as OverlayFS widely used in Docker environments, path translation overhead can multiply the number of disk operations required. In practice, eBPF tracing allows us to measure the exact time the request spends in each intermediate queue, separating wait time from actual hardware processing time.

Building efficient probes for latency metric collection

The practical implementation of performance analysis with eBPF requires specialized tools that simplify writing programs for the kernel. Open-source projects like BCC and bpftrace provide a high-level language inspired by awk that allows creating complex tracing scripts in just a few lines of code. In practice, we can write a script that monitors all kernel function calls related to the block queue and groups the results into a latency histogram.

Below we present a conceptual example script using bpftrace syntax to track the distribution of disk write latency, helping identify which processes are causing greater contention on ephemeral storage:

#include <linux/blkdev.h>

kprobe:blk_account_io_start
{
    @start[arg0] = nsecs;
}

kprobe:blk_account_io_done
/@start[arg0]/
{
    $lat = (nsecs - @start[arg0]) / 1000;
    @lat_us = hist($lat);
    delete(@start[arg0]);
}

This simple code monitors the exact moment a disk operation starts and ends, calculating the difference in microseconds and organizing the data into visual bins. In practice, this immediately reveals whether storage latency is distributed evenly or if there are isolated spikes harming service stability.

Interpreting histograms and identifying contention points

Collecting raw data through eBPF is only the first step of the engineering process; true skill lies in the correct interpretation of the generated histograms. When analyzing IOPS latency distribution, we look for bimodal patterns, which indicate that most operations are extremely fast while a significant portion suffers drastic delays due to queue saturation. In practice, this behavior typically highlights bandwidth limits imposed by the cloud provider or local storage controller.

Another critical indicator is I/O queue depth, which represents how many requests are waiting for their turn to be processed by the disk. If queue depth remains consistently high while IOPS throughput plateaus, we have mathematical confirmation of a saturation bottleneck. In this scenario, increasing application concurrency brings no performance gain; on the contrary, it worsens the situation by further overloading kernel buffers with requests that will be dropped or delayed.

Mitigating ephemeral storage bottlenecks in production

With precise diagnostics in hand provided by kernel tracing tools, we can apply targeted countermeasures to resolve IOPS issues. A common approach consists of adjusting container resource limits using native Linux cgroups mechanisms, ensuring noisy applications do not consume all available write bandwidth on the node. In practice, limiting the volume of temporary data written to ephemeral layers forces the use of dedicated volumes or in-memory structures when speed is critical.

Another architectural strategy involves reviewing the storage driver type and the underlying file system cache policies. Replacing default configurations with options optimized for asynchronous writes or disabling unnecessary journaling features on test volumes can drastically eliminate background I/O operations. In practice, each structural adjustment reduces pressure on the kernel, ensuring data flows smoothly and predictably.

Final considerations on low-level observability

Investigating IOPS bottlenecks in ephemeral storage layers stops being a guessing game when we adopt advanced instrumentation techniques based on eBPF. Understanding the journey of data from the system call down to the block subsystem empowers engineering teams to solve complex performance issues backed by undeniable mathematical evidence. In practice, mastering these kernel tracing tools elevates any organization's operational maturity, transforming mysterious infrastructure blackouts into predictable and easily solvable incidents.