# Examining Load Average

The load-average calculation is best thought of as a moving average of processes in Linux's run queue marked running or uninterruptible. The words “thought of” were chosen for a reason: that is how the measurements are meant to be interpreted, but not exactly what happens behind the curtain. It is at this juncture in our journey when the reality of it all, like quantum mechanics, seems not to fit the intuitive way as it presents itself.

The load averages that the top and uptime commands display are
obtained directly from /proc. If you are running Linux kernel 2.4
or later, you can read those values yourself with the command `cat
/proc/loadavg`. However, it is the Linux kernel that produces those
values in /proc. Specifically, timer.c and sched.h work together to
do the computation. To understand what timer.c does for a living, the
concept of time slicing and the jiffy counter help round out the picture.

In the Linux kernel, each dispatchable process is given a fixed amount of time on the CPU per dispatch. By default, this amount is 10 milliseconds, or 1/100th of a second. For that short time span, the process is assigned a physical CPU on which to run its instructions and allowed to take over that processor. More often than not, the process will give up control before the 10ms are up through socket calls, I/O calls or calls back to the kernel. (On an Intel 2.6GHz processor, 10ms is enough time for approximately 50-million instructions to occur. That's more than enough processing time for most application cycles.) If the process uses its fully allotted CPU time of 10ms, an interrupt is raised by the hardware, and the kernel regains control from the process. The kernel then promptly penalizes the process for being such a hog. As you can see, that time slicing is an important design concept for making your system seem to run smoothly on the outside. It also is the vehicle that produces the load-average values.

The 10ms time slice is an important enough concept to warrant a name for itself: quantum value. There is not necessarily anything inherently special about 10ms, but there is about the quantum value in general, because whatever value it is set to (it is configurable, but 10ms is the default), it controls how often at a minimum the kernel takes control of the system back from the applications. One of the many chores the kernel performs when it takes back control is to increment its jiffies counter. The jiffies counter measures the number of quantum ticks that have occurred since the system was booted. When the quantum timer pops, timer.c is entered at a function in the kernel called timer.c:do_timer(). Here, all interrupts are disabled so the code is not working with moving targets. The jiffies counter is incremented by 1, and the load-average calculation is checked to see if it should be computed. In actuality, the load-average computation is not truly calculated on each quantum tick, but driven by a variable value that is based on the HZ frequency setting and tested on each quantum tick. (HZ is not to be confused with the processor's MHz rating. This variable sets the pulse rate of particular Linux kernel activity and 1HZ equals one quantum or 10ms by default.) Although the HZ value can be configured in some versions of the kernel, it is normally set to 100. The calculation code uses the HZ value to determine the calculation frequency. Specifically, the timer.c:calc_load() function will run the averaging algorithm every 5 * HZ, or roughly every five seconds. Following is that function in its entirety:

unsigned long avenrun[3]; static inline void calc_load(unsigned long ticks) { unsigned long active_tasks; /* fixed-point */ static int count = LOAD_FREQ; count -= ticks; if (count < 0) { count += LOAD_FREQ; active_tasks = count_active_tasks(); CALC_LOAD(avenrun[0], EXP_1, active_tasks); CALC_LOAD(avenrun[1], EXP_5, active_tasks); CALC_LOAD(avenrun[2], EXP_15, active_tasks); } }

The avenrun array contains the three averages we have been discussing. The calc_load() function is called by update_times(), also found in timer.c, and is the code responsible for supplying the calc_load() function with the ticks parameter. Unfortunately, this function does not reveal its most interesting aspect: the computation itself. However, that can be located easily in sched.h, a header used by much of the kernel code. In there, the CALC_LOAD macro and its associated values are available:

extern unsigned long avenrun[]; /* Load averages */ #define FSHIFT 11 /* nr of bits of precision */ #define FIXED_1 (1<<FSHIFT) /* 1.0 as fixed-point */ #define LOAD_FREQ (5*HZ) /* 5 sec intervals */ #define EXP_1 1884 /* 1/exp(5sec/1min) as fixed-point */ #define EXP_5 2014 /* 1/exp(5sec/5min) */ #define EXP_15 2037 /* 1/exp(5sec/15min) */ #define CALC_LOAD(load,exp,n) \ load *= exp; \ load += n*(FIXED_1-exp); \ load >>= FSHIFT;

Here is where the tires meet the pavement. It should now be evident that reality does not appear to match the illusion. At least, this is certainly not the type of averaging most of us are taught in grade school. But it is an average nonetheless. Technically, it is an exponential decay function and is the moving average of choice for most UNIX systems as well as Linux. Let's examine its details.

The macro takes in three parameters: the load-average bucket (one of the three elements in avenrun[]), a constant exponent and the number of running/uninterruptible processes currently on the run queue. The possible exponent constants are listed above: EXP_1 for the 1-minute average, EXP_5 for the 5-minute average and EXP_15 for the 15-minute average. The important point to notice is that the value decreases with age. The constants are magic numbers that are calculated by the mathematical function shown below:

When x=1, then y=1884; when x=5, then y=2014; and when x=15, then y=2037. The purpose of the magical numbers is that it allows the CALC_LOAD macro to use precision fixed-point representation of fractions. The magic numbers are then nothing more than multipliers used against the running load average to make it a moving average. (The mathematics of fixed-point representation are beyond the scope of this article, so I will not attempt an explanation.) The purpose of the exponential decay function is that it not only smooths the dips and spikes by maintaining a useful trend line, but it accurately decreases the quality of what it measures as activity ages. As time moves forward, successive CPU events increase their significance on the load average. This is what we want, because more recent CPU activity probably has more of an impact on the current state than ancient events. In the end, the load averages give a smooth trend from 15 minutes through the current minute and give us a window into not only the CPU usage but also the average demand for the CPUs. As the load average goes above the number of physical CPUs, the more the CPU is being used and the more demand there is for it. And, as it recedes, the less of a demand there is. With this understanding, the load average can be used with the CPU percentage to obtain a more accurate view of CPU activity.

It is my hope that this serves not only as a practical interpretation of Linux's load averages but also illuminates some of the dark mathematical shadows behind them. For more information, a study of the exponential decay function and its applications would shed more light on the subject. But for the more practical-minded, plotting the load average vs. a controlled number of processes (that is, modeling the effects of the CALC_LOAD algorithm in a controlled loop) would give you a feel for the actual relationship and how the decaying filter applies.

Ray Walker is a consultant specializing in UNIX kernel-level code. He has been a software developer for more than 25 years, working with Linux since 1995. He can be contacted at ray.rwalk2730@gmail.com.

## Trending Topics

openHAB | Apr 24, 2017 |

Omesh Tickoo and Ravi Iyer's Making Sense of Sensors (Apress) | Apr 21, 2017 |

Low Power Wireless: 6LoWPAN, IEEE802.15.4 and the Raspberry Pi | Apr 20, 2017 |

CodeLathe's Tonido Personal Cloud | Apr 19, 2017 |

Wrapping Up the Mars Lander | Apr 18, 2017 |

MultiTaction's MT Canvus-Connect | Apr 17, 2017 |

- openHAB
- Teradici's Cloud Access Platform: "Plug & Play" Cloud for the Enterprise
- The Weather Outside Is Frightful (Or Is It?)
- Low Power Wireless: 6LoWPAN, IEEE802.15.4 and the Raspberry Pi
- Understanding Firewalld in Multi-Zone Configurations
- Simple Server Hardening
- Bash Shell Script: Building a Better March Madness Bracket
- Non-Linux FOSS: Control Web-Based Music!
- Gordon H. Williams' Making Things Smart (Maker Media, Inc.)
- SNMP

## Geek Guides

Pick up any e-commerce web or mobile app today, and you’ll be holding a mashup of interconnected applications and services from a variety of different providers. For instance, when you connect to Amazon’s e-commerce app, cookies, tags and pixels that are monitored by solutions like Exact Target, BazaarVoice, Bing, Shopzilla, Liveramp and Google Tag Manager track every action you take. You’re presented with special offers and coupons based on your viewing and buying patterns. If you find something you want for your birthday, a third party manages your wish list, which you can share through multiple social- media outlets or email to a friend. When you select something to buy, you find yourself presented with similar items as kind suggestions. And when you finally check out, you’re offered the ability to pay with promo codes, gifts cards, PayPal or a variety of credit cards.

Get the Guide
## Comments

## load ave

I have been trying to figure out what those 3 numbers meant. Thanks for the article. How does hyperthreading play into the number of lanes? Is that additive or do I just count the total # of cores?

## Thanks for the article

It is really good article, till now following load avg values blindly.

This article given clear understanding.

## Thank you for the article,

Thank you for the article, now i've understood the difference.

But what about hyperthreading?

## VPS environment

I'm curious, how does top report load average on a VPS? is it reporting on the VPS's average or the server machine's?

Just thought I put it out there.. thanks for any replies.

## great article

this certainly answered a lot of questions for me. great article. thanks!

## How to get CPU load inf in terms of Percentage

CPU load average, we can get using many commands, but how to get this average value in terms of percentage

Is there any formula for it? searched in google couldnt get right information on how to get this percentile value

Thanks

Vikram

## multi core procs

So how does this apply to multi core cpu's?

If I have 4 quad core cpu's in my system then do I still only have 4 lanes on my highway? or do I have 16?

Thanks,

Dave

## You have one lane per core in

You have one lane per core in the system.

So a system with 4 CPUs, each with one core would be a 4-lane highway (as stated in the article) - or you can think of it as 4 single lane highways.

If you have a single quad-core then you have a 4-lane highway.

If you have 4 quad-core CPUs, then you have 4 4-land highways (or a single 16-lane highway).

## Nice & useful article ! I'd

Nice & useful article !

I'd like to mention here that the timeslices of each task are NOT 10 ms, but 100 ms as an average (nice val 0) & goes down to 10 ms (assuming HZ=100) for nice val -19.

This is ofcourse for the pre-CFS days, which is when I believe the article was written

## Load Average

Always had a confusion about load avg, thanks to your article it is clear now. Great Article