Linux I/O Schedulers

In this sixth part of a seven-part series on the Linux I/O file system, you’ll learn about the importance of I/O schedulers, and the various different types. This article is excerpted from chapter four of the book Linux System Programming: Talking Directly to the Kernel and C Library, written by Robert Love (O’Reilly, 2007; ISBN: 0596009585). Copyright © 2007 O’Reilly Media, Inc. All rights reserved. Used with permission from the publisher. Available from booksellers or direct from O’Reilly Media.

I/O Schedulers and I/O Performance

In a modern system, the relative performance gap between disks and the rest of the system is quite large—and widening. The worst component of disk performance is the process of moving the read/write head from one part of the disk to another, an operation known as a seek. In a world where many operations are measured in a handful of processor cycles (which might take all of a third of a nanosecond each), a single disk seek can average over eight milliseconds—still a small number, to be sure, but 25 million times longer than a single processor cycle!

Given the disparity in performance between disk drives and the rest of the system, it would be incredibly crude and inefficient to send I/O requests to the disk in the order in which they are issued. Therefore, modern operating system kernels implement I/O schedulers, which work to minimize the number and size of disk seeks by manipulating the order in which I/O requests are serviced, and the times at which they are serviced. I/O schedulers work hard to lessen the performance penalties associated with disk access.

Disk Addressing

To understand the role of an I/O scheduler, some background information is necessary. Hard disks address their data using the familiar geometry-based addressing of cylinders, heads, and sectors, or CHS addressing. A hard drive is composed of multiple platters, each consisting of a single disk, spindle, and read/write head. You can think of each platter as a CD (or record), and the set of platters in a disk as a stack of CDs. Each platter is divided into circular ring-like tracks, like on a CD. Each track is then divided up into of an integer number of sectors.

To locate a specific unit of data on a disk, the drive’s logic requires three pieces of information: the cylinder, head, and sector values. The cylinder value specifies the track on which the data resides. If you lay the platters on top of one another, a given track forms a cylinder through each platter. In other words, a cylinder is represented by a track at the same distance from the center on each disk. The head value identifies the exact read/write head (and thus the exact platter) in question. The search is now narrowed down to a single track on a single platter. The disk then uses the sector value to identify an exact sector on the track. The search is now complete: the hard disk knows what platter, what track, and what sector to look in for the data. It can position the read/write head of the correct platter over the correct track, and read from or write to the requisite sector.

Thankfully, modern hard disks do not force computers to communicate with their disks in terms of cylinders, heads, and sectors. Instead, contemporary hard drives map a unique block number (also called physical blocks or device blocks) over each cylinder/head/sector triplet—effectively, a block maps to a specific sector. Modern operating systems can then address hard drives using these block numbers—a process known as logical block addressing (LBA)—and the hard drive internally translates the block number into the correct CHS address.* Although nothing guarantees it, the block-to-CHS mapping tends to be sequential: physical block n tends to be physically adjacent on disk to logical block n + 1. This sequential mapping is important, as we shall soon see.

Filesystems, meanwhile, exist only in software. They operate on their own units, known as logical blocks (sometimes called filesystem blocks, or, confusingly, just blocks). The logical block size must be an integer multiple of the physical block size. In other words, a filesystem’s logical blocks map to one or more of a disk’s physical blocks.

{mospagebreak title=The Life of an I/O Scheduler}

I/O schedulers perform two basic operations: merging and sorting. Merging is the process of taking two or more adjacent I/O requests, and combining them into a single request. Consider two requests, one to read from disk block 5, and another to read from disk blocks 6 through 7. These requests can be merged into a single request to read from disk blocks 5 through 7. The total amount of I/O might be the same, but the number of I/O operations is reduced by half.

Sorting, the more important of the two operations, is the process of arranging pending I/O requests in ascending block order. For example, given I/O operations to blocks 52, 109, and 7, the I/O scheduler would sort these requests into the ordering 7, 52, and 109. If a request was then issued to block 81, it would be inserted between the requests to blocks 52 and 109. The I/O scheduler would then dispatch the requests to the disk in the order that they exist in the queue: 7, then 52, then 81, and finally 109.

In this manner, the disk head’s movements are minimized. Instead of potentially haphazard movements—here to there and back, seeking all over the disk—the disk head moves in a smooth, linear fashion. Because seeks are the most expensive part of disk I/O, performance is improved.

Helping Out Reads

Each read request must return up-to-date data. Thus, if the requested data is not in the page cache, the reading process must block until the data can be read from disk—a potentially lengthy operation. We call this performance impact read latency.

A typical application might initiate several read I/O requests in a short period. Because each request is individually synchronized, the later requests are dependent on the earlier ones’ completion. Consider reading every file in a directory. The application opens the first file, reads a chunk of it, waits for data, reads another chunk, and so on, until the entire file is read. Then the application starts again, on the next file. The requests become serialized: a subsequent request cannot be issued until the current request completes.

This is in stark contrast to write requests, which (in their default, nonsynchronized state) need not initiate any disk I/O until some time in the future. Thus, from the perspective of a user-space application, write requests stream, unencumbered by the performance of the disk. This streaming behavior only compounds the problem for reads: as writes stream, they can hog the kernel and disk’s attention. This phenomenon is known as the writes-starving-reads problem.

If an I/O scheduler always sorted new requests by the order of insertion, it would be possible to starve requests to far-off blocks indefinitely. Consider our previous example. If new requests were continually issued to blocks in, say, the 50s, the request to block 109 would never be serviced. Because read latency is critical, this behavior would greatly hurt system performance. Thus, I/O schedulers employ a mechanism to prevent starvation.

A simple approach—such as the one taken by the 2.4 Linux kernel’s I/O scheduler, the Linus Elevator*—is to simply stop insertion-sorting if there is a sufficiently old request in the queue. This trades overall performance for per-request fairness and, in the case of reads, improves latency. The problem is that this heuristic is a bit too simplistic. Recognizing this, the 2.6 Linux kernel witnessed the demise of the Linus Elevator, and unveiled several new I/O schedulers in its place.

{mospagebreak title=The Deadline I/O Scheduler}

The Deadline I/O Scheduler was introduced to solve the problems with the 2.4 I/O scheduler, and traditional elevator algorithms in general. The Linus Elevator maintains a sorted list of pending I/O requests. The I/O request at the head of the queue is the next one to be serviced. The Deadline I/O Scheduler keeps this queue, but kicks things up a notch by introducing two additional queues: the read FIFO queue, and the write FIFO queue. The items in each of these queues are sorted by submission time (effectively, the first in is the first out). The read FIFO queue, as its name suggests, contains only read requests. The write FIFO queue, likewise, contains only write requests. Each request in the FIFO queues is assigned an expiration value. The read FIFO queue has an expiration time of 500 milliseconds. The write FIFO queue has an expiration time of five seconds.

When a new I/O request is submitted, it is insertion-sorted into the standard queue, and placed at the tail of its respective (read or write) FIFO queue. Normally, the hard drive is sent I/O requests from the head of the standard sorted queue. This maximizes global throughput by minimizing seeks, as the normal queue is sorted by block number (as with the Linus Elevator).

When the item at the head of one of the FIFO queues grows older than the expiration value associated with its queue, however, the I/O scheduler stops dispatching I/O requests from the standard queue, and begins servicing requests from that queue—the request at the head of the FIFO queue is serviced, plus a couple of extras for good measure. The I/O scheduler needs to check and handle only the requests at the head of the queue, as those are the oldest requests.

In this manner, the Deadline I/O Scheduler can enforce a soft deadline on I/O requests. Although it makes no promise that an I/O request will be serviced before its expiration time, the I/O scheduler generally services requests near their expiration times. Thus, the Deadline I/O Scheduler continues to provide good global throughput without starving any one request for an unacceptably long time. Because read requests are given shorter expiration times, the writes-starving-reads problem is minimized.

The Anticipatory I/O Scheduler

The Deadline I/O Scheduler’s behavior is good, but not perfect. Recall our discussion on read dependency. With the Deadline I/O Scheduler, the first read request in a series of reads is serviced in short order, at or before its expiration time, and the I/O scheduler then returns to servicing I/O requests from the sorted queue—so far, so good. But suppose the application then swoops in and hits us with another read request? Eventually its expiration time will also approach, and the I/O scheduler will submit it to the disk, which will seek over to promptly handle the request, then seek back to continue handling requests from the sorted queue. This seeking back and forth can continue for some time because many applications exhibit this behavior. While latency is kept to a minimum, global throughput is not very good because the read requests keep coming in, and the disk has to keep seeking back and forth to handle them. Performance would be improved if the disk just took a break to wait for another read, and did not move away to service the sorted queue again. But, unfortunately, by the time the application is scheduled and submits its next dependent read request, the I/O scheduler has already shifted gears.

The problem again stems from those darn dependent reads—each new read request is issued only when the previous one is returned, but by the time the application receives the read data, is scheduled to run, and submits its next read request, the I/O scheduler has moved on, and begun servicing other requests. This results in a wasted pair of seeks for each read: the disk seeks to the read, services it, and then seeks back. If only there was some way for the I/O scheduler to know—to anticipate—that another read would soon be submitted to the same part of the disk, instead of seeking back and forth, it could wait in anticipation of the next read. Saving those awful seeks certainly would be worth a few milliseconds of waiting.

This is exactly how the Anticipatory I/O Scheduler operates. It began life as the Deadline I/O Scheduler, but was gifted with the addition of an anticipation mechanism. When a read request is submitted, the Anticipatory I/O Scheduler services it within its deadline, as usual. Unlike the Deadline I/O Scheduler, however, the Anticipatory I/O Scheduler then sits and waits, doing nothing, for up to six milliseconds. Chances are good that the application will issue another read to the same part of the filesystem during those six milliseconds. If so, that request is serviced immediately, and the Anticipatory I/O Scheduler waits some more. If six milliseconds go by without a read request, the Anticipatory I/O Scheduler decides it has guessed wrong, and returns to whatever it was doing before (i.e., servicing the standard sorted queue). If even a moderate number of requests are anticipated correctly, a great deal of time—two expensive seeks’ worth at each go—is saved. Because most reads are dependent, the anticipation pays off much of the time.

{mospagebreak title=The CFQ I/O Scheduler}

The Complete Fair Queuing (CFQ) I/O Scheduler works to achieve similar goals, albeit via a different approach.* With CFQ, each process is assigned its own queue, and each queue is assigned a timeslice. The I/O scheduler visits each queue in a round-robin fashion, servicing requests from the queue until the queue’s timeslice is exhausted, or until no more requests remain. In the latter case, the CFQ I/O Scheduler will then sit idle for a brief period—by default, 10 ms—waiting for a new request on the queue. If the anticipation pays off, the I/O scheduler avoids seeking. If not, the waiting was in vain, and the scheduler moves on to the next process’ queue.

Within each process’ queue, synchronized requests (such as reads) are given priority over nonsynchronized requests. In this manner, CFQ favors reads and prevents the writes-starving-reads problem. Because of the per-process queue setup, the CFQ I/O Scheduler is fair to all processes, while still providing good global performance.

The CFQ I/O Scheduler is well suited to most workloads, and makes an excellent first choice.

The Noop I/O Scheduler

The Noop I/O Scheduler is the most basic of the available schedulers. It performs no sorting whatsoever, only basic merging. It is used for specialized devices that do not require (or that perform) their own request sorting.


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