Defining Unix Load Average

// November 7th, 2005 // Technology Bits

Went looking for a simple definition of load average in Unix, but alas, simplicity is not in the nature of load. I read UNIX® Load Average Part 1: How It Works by Dr. Neil Gunther who breaks load down into much appreciated, yet excruciating detail.

In short it is the average sum of the number of processes waiting in the run-queue plus the number currently executing over 1, 5, and 15 minute time periods.

It’s calculated like this: load(t) = load(t – 1) e^(-5/60m) + n (1 – e^(-5/60m))
(at least in Linux)

From Gunther’s PDF Guide:

Most sys admins tend to refer to and use the m = 1 minute load average For queueing models we want the steady-state average [...] that suggests the m = 15 minute load average is more useful for capacity planning

So load is useful but complex.

Casey experienced some pretty sever load numbers recently…

definition, dr. neil gunther, gunther, LA triplets, linux top, load, load average, monitoring, performance, performance monitoring, solaris, unix

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5 Responses to “Defining Unix Load Average”

  1. Life After Coffee » UNIX Load Averages Explained says:
    March 13, 2006 at 3:44 pm

    [...] In his blog entry from late last year, Zach sums it up quite nicely: In short it is the average sum of the number of processes waiting in the run-queue plus the number currently executing over 1, 5, and 15 minute time periods. [...]

  2. Tadek’s Blog » Blog Archive » load average says:
    July 5, 2006 at 3:39 pm

    [...] Have you ever wondered how load average on Unix is defined? [...]

  3. Denis says:
    September 17, 2006 at 6:56 am

    Friend,

    This information is wrong. You forgot the “m” (minute) information:

    -5/60m

    Attention!!!

    Hugs,
    Denis

  4. José Morelli Neto » O que é o Load Average says:
    September 18, 2006 at 6:29 pm

    [...] Defining Unix Load Average [...]

  5. JMK says:
    May 9, 2010 at 10:38 pm

    My opinions:

    If load averages > cpu numbers,the server is overload.
    Then we can check the cpu/mem/io from top.
    It’s not very difficult to find which one is the bottleneck.

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