BrainDump
  Home arrow BrainDump arrow Page 3 - Linux I/O Schedulers
Dev Shed Forums  
Administration  
AJAX  
Apache  
BrainDump  
DHTML  
Flash  
Java  
JavaScript  
Multimedia  
MySQL  
Oracle  
Perl  
PHP  
Practices  
Python  
Reviews  
Security  
Smartphone Development  
Style-Sheets  
Web Services  
XML  
Zend  
Zope  
Mobile Linux  
App Generation ROI  
IBM® developerWorks  
Forums Sitemap  
E-Commerce Hosting  
Linux Web Hosting  
Managed Hosting  
Small Business Hosting  
VPS Hosting  
Weekly Newsletter

 
Developer Updates  
Free Website Content 
 RSS  Articles
 RSS  Forums
 RSS  All Feeds
Write For Us Get Paid  
Request Media Kit
Contact Us  
Site Map  
Privacy Policy  
Support  
 USERNAME
 
 PASSWORD
 
 
  >>> SIGN UP!  
  Lost Password? 
BRAINDUMP

Linux I/O Schedulers
By: O'Reilly Media
  • Search For More Articles!
  • Disclaimer
  • Author Terms
  • Rating: starstarstarstarstar / 6
    2008-12-31


    Table of Contents:
  • Linux I/O Schedulers
  • The Life of an I/O Scheduler
  • The Deadline I/O Scheduler
  • The CFQ I/O Scheduler

  • Rate this Article: Poor Best 
      ADD THIS ARTICLE TO:
      error-file:tidyout.log Del.ici.ous error-file:tidyout.log Digg
      error-file:tidyout.log Blink error-file:tidyout.log Simpy
      error-file:tidyout.log Google error-file:tidyout.log Spurl
      error-file:tidyout.log Y! MyWeb error-file:tidyout.log Furl
    Email Me Similar Content When Posted
    Add Developer Shed Article Feed To Your Site
    Email Article To Friend
    Print Version Of Article
    PDF Version Of Article

     
     
    ADVERTISEMENT


    Linux I/O Schedulers - The Deadline I/O Scheduler
    ( Page 3 of 4 )

    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.



     
     
    >>> More BrainDump Articles          >>> More By O'Reilly Media
     

       

    BRAINDUMP ARTICLES

    - Demystifying SELinux on Kernel 2.6
    - Yahoo and Microsoft Create Ad Partnership
    - The Advantages of Obscure Open Source Browse...
    - Dell Announces CSI-style Digital Forensics S...
    - Milepost GCC Speeds Open-Source Development
    - Learn These 10 Programming Languages
    - Tomcat Capacity Planning
    - Internal and External Performance Tuning wit...
    - Tomcat Benchmark Procedure
    - Benchmarking Tomcat Performance
    - Tomcat Performance Tuning
    - Wubi: Windows-based Ubuntu Installer
    - Configuring and Optimizing Your I/O Scheduler
    - Linux I/O Schedulers
    - Advising the Linux Kernel on File I/O





    © 2003-2009 by Developer Shed. All rights reserved. DS Cluster 2 Hosted by Hostway
    Stay green...Green IT