[转载] Linux/Unix 系统负载原理解析[英文]

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CALC_LOAD() calc_load()工作原理

In this two part-series I want to explore the use of averages in performance analysis and capacity planning. There are many manifestations of averages e.g., arithmetic average (the usual one), moving average (often used in financial planning), geometric average (used in theSPEC CPU benchmarks), harmonic average (not used enough), to name a few.

More importantly, we will be looking at averages over time or time-dependent averages. A particular example of such a time-dependent average is the load average metric that appears in certain UNIX commands. In Part 1 I shall look at what the load average is and how it gets calculated. In Part 2 I‘ll compare it with other averaging techniques as they apply in capacity planning and performance analysis. This article does not assume you are a familiar with UNIX commands, so I will begin by reviewing those commands which display the load average metric. By Section 4, however, I‘ll be submerging into the UNIX kernel code that does all the work.

1 UNIX Commands

Actually, load average is not a UNIX command in the conventional sense. Rather it‘s an embedded metric that appears in the output of other UNIX commands like uptime andprocinfo. These commands are commonly used by UNIX sysadmin‘s to observe system resource consumption. Let‘s look at some of them in more detail.

1.1  Classic Output

The generic ASCII textual format appears in a variety of UNIX shell commands. Here are some common examples.

uptime

The uptime shell command produces the following output:

[pax:~]% uptime9:40am  up 9 days, 10:36,  4 users,  load average: 0.02, 0.01, 0.00

It shows the time since the system was last booted, the number of active user processes and something called the load average.

procinfo

On Linux systems, the procinfo command produces the following output:

[pax:~]% procinfoLinux 2.0.36 ([email protected]) (gcc 2.7.2.3) #1 Wed Jul 25 21:40:16 EST 2001 [pax]

Memory:      Total        Used        Free      Shared     Buffers      CachedMem:         95564       90252        5312       31412       33104       26412Swap:        68508           0       68508

Bootup: Sun Jul 21 15:21:15 2002    Load average: 0.15 0.03 0.01 2/58 8557...

The load average appears in the lower left corner of this output.

w

The w(ho) command produces the following output:

[pax:~]% w  9:40am  up 9 days, 10:35,  4 users,  load average: 0.02, 0.01, 0.00USER     TTY      FROM              [email protected]   IDLE   JCPU   PCPU  WHATmir      ttyp0    :0.0             Fri10pm  3days  0.09s  0.09s  bashneil     ttyp2    12-35-86-1.ea.co  9:40am  0.00s  0.29s  0.15s  w...

Notice that the first line of the output is identical to the output of the uptime command.

top

The top command is a more recent addition to the UNIX command set that ranks processes according to the amount of CPU time they consume. It produces the following output:

  4:09am  up 12:48,  1 user,  load average: 0.02, 0.27, 0.1758 processes: 57 sleeping, 1 running, 0 zombie, 0 stoppedCPU states:  0.5% user,  0.9% system,  0.0% nice, 98.5% idleMem:   95564K av,  78704K used,  16860K free,  32836K shrd,  40132K buffSwap:  68508K av,      0K used,  68508K free                 14508K cched

  PID USER     PRI  NI  SIZE  RSS SHARE STAT  LIB %CPU %MEM   TIME COMMAND 5909 neil      13   0   720  720   552 R       0  1.5  0.7   0:01 top    1 root       0   0   396  396   328 S       0  0.0  0.4   0:02 init    2 root       0   0     0    0     0 SW      0  0.0  0.0   0:00 kflushd    3 root     -12 -12     0    0     0 SW<     0  0.0  0.0   0:00 kswapd...

In each of these commands, note that there are three numbers reported as part of the load average output. Quite commonly, these numbers show a descending order from left to right. Occasionally, however, an ascending order appears e.g., like that shown in the top output above.

1.2  GUI Output

The load average can also be displayed as a time series like that shown here in some output from a tool called ORCA.

Figure 1: ORCA plot of the 3 daily load averages.

Although such visual aids help us to see that the green curve is more spikey and has more variability than the red curve, and it allows us to see a complete day‘s worth of data, it‘s not clear how useful this is for capacity planning or performance analysis. We need to understand more about how the load average metric is defined and calculated.

2  So What Is It?

So, exactly what is this thing called load average that is reported by all these various commands? Let‘s look at the official UNIX documentation.

2.1  The man Page

[pax:~]% man "load average"No manual entry for load average

Oops! There is no man page! The load average metric is an output embedded in other commands so it doesn‘t get its own man entry. Alright, let‘s look at the man page for uptime, for example, and see if we can learn more that way.

...DESCRIPTION       uptime  gives a one line display of the following informa-       tion.  The current time, how long the system has been run-       ning, how many users are currently logged on, and the sys-       tem load averages for the past 1, 5, and 15 minutes....

So, that explains the three metrics. They are the "... load averages for the past 1, 5, and 15 minutes."

Unfortunately, that still begs the question "What is the load?

2.2  What the Gurus Have to Say

Let‘s turn to some UNIX hot-shots for more enlightenment.

Tim O‘Reilly and Crew

The book UNIX Power Tools [POL97], tell us on p.726 The CPU:

That‘s encouraging! Anyway, it does help to explain what is being measured: the number of active processes. On p.720 39.07 Checking System Load: uptime it continues ...

Hmmm ... where did that number "3" come from? And which of the three averages (1, 5, 15 minutes) are they referring to?

Adrian Cockcroft on Solaris

In Sun Performance and Tuning [Coc95] in the section on p.97 entitled: Understanding and Using the Load Average, Adrian Cockcroft states:

So, even the "big boys" at Sun can get it wrong. Nonetheless, the idea that the load average is associated with the CPU run queue is an important point.

O‘Reilly et al. also note some potential gotchas with using load average ...

As I will demonstrate, this depends on when you look. If the CPU-bound process runs long enough, it will drive the load average up because its always either running or runable. The obscurities stem from the fact that the load average is not your average kind of average. As we alluded to in the above introduction, it‘s a time-dependentaverage. Not only that, but it‘s adamped time-dependent average. To find out more, let‘s do some controlled experiments.

3  Performance Experiments

The experiments described in this section involved running some workloads in background on single-CPU Linux box. There were two phases in the test which has a duration of 1 hour:

• CPU was pegged for 2100 seconds and then the processes were killed.
• CPU was quiescent for the remaining 1500 seconds.

A Perl script sampled the load average every 5 minutes using the uptime command. Here are the details.

3.1  Test Load

Two hot-loops were fired up as background tasks on a single CPU Linux box. There were two phases in the test:

1. The CPU is pegged by these tasks for 2,100 seconds.
2. The CPU is (relatively) quiescent for the remaining 1,500 seconds.

The 1-minute average reaches a value of 2 around 300 seconds into the test. The 5-minute average reaches 2 around 1,200 seconds into the test and the 15-minute average would reach 2 at around 3,600 seconds but the processes are killed after 35 minutes (i.e., 2,100 seconds).

3.2  Process Sampling

As the authors [BC01] explain about the Linux kernel, because both of our test processes are CPU-bound they will be in a TASK_RUNNING state. This means they are either:

• running i.e., currently executing on the CPU
• runnable i.e., waiting in the run_queue for the CPU

The Linux kernel also checks to see if there are any tasks in a short-term sleep state calledTASK_UNINTERRUPTIBLE. If there are, they are also included in the load average sample. There were none in our test load.

The following source fragment reveals more details about how this is done.

600  * Nr of active tasks - counted in fixed-point numbers601  */602 static unsigned long count_active_tasks(void)603 {604         struct task_struct *p;605         unsigned long nr = 0;606607         read_lock(&tasklist_lock);608         for_each_task(p) {609                 if ((p->state == TASK_RUNNING ||610                      (p->state & TASK_UNINTERRUPTIBLE)))611                         nr += FIXED_1;612         }613         read_unlock(&tasklist_lock);614         return nr;615 }

So, uptime is sampled every 5 seconds which is the linux kernel‘s intrinsic timebase for updating the load average calculations.

3.3  Test Results

The results of these experiments are plotted in Fig. 2. NOTE: These colors do not correspond to those used in the ORCA plots like Figure 1.

Although the workload starts up instantaneously and is abruptly stopped later at 2100 seconds, the load average values have to catch up with the instantaneous state. The 1-minute samples track the most quickly while the 15-minute samples lag the furthest.

 
Figure 2: Linux load average test results.

For comparison, here‘s how it looks for a single hot-loop running on a single-CPU Solaris system.

 
Figure 3: Solaris load average test results.

You would be forgiven for jumping to the conclusion that the "load" is the same thing as the CPU utilization. As the Linux results show, when two hot processes are running, the maximum load is two (not one) on a single CPU. So, load is not equivalent to CPU utilization.

From another perspective, Fig. 2 resembles the charging

 
Figure 4: Charging and discharging of a capacitor.

and discharging of a capacitive RC circuit.

4 Kernel Magic

An Addendum

Now let‘s go inside the Linux kernel and see what it is doing to generate these load average numbers.

unsigned long avenrun[3];624625 static inline void calc_load(unsigned long ticks)626 {627         unsigned long active_tasks; /* fixed-point */628         static int count = LOAD_FREQ;629630         count -= ticks;631         if (count < 0) {632                 count += LOAD_FREQ;633                 active_tasks = count_active_tasks();634                 CALC_LOAD(avenrun[0], EXP_1, active_tasks);635                 CALC_LOAD(avenrun[1], EXP_5, active_tasks);636                 CALC_LOAD(avenrun[2], EXP_15, active_tasks);637         }638 }

The countdown is over a LOAD_FREQ of 5 HZ. How often is that?

  1 HZ    =   100 ticks  5 HZ    =   500 ticks  1 tick  =    10 milliseconds500 ticks =  5000 milliseconds (or 5 seconds)

So, 5 HZ means that CALC_LOAD is called every 5 seconds.

4.1  Magic Numbers

The function CALC_LOAD is a macro defined in sched.h

58 extern unsigned long avenrun[];         /* Load averages */5960 #define FSHIFT          11              /* nr of bits of precision */61 #define FIXED_1         (1<<FSHIFT)     /* 1.0 as fixed-point */62 #define LOAD_FREQ       (5*HZ)          /* 5 sec intervals */63 #define EXP_1           1884            /* 1/exp(5sec/1min) as fixed-point */64 #define EXP_5           2014            /* 1/exp(5sec/5min) */65 #define EXP_15          2037            /* 1/exp(5sec/15min) */6667 #define CALC_LOAD(load,exp,n) \68         load *= exp; \69         load += n*(FIXED_1-exp); \70         load >>= FSHIFT;

A noteable curiosity is the appearance of those magic numbers: 1884, 2014, 2037. What do they mean? If we look at the preamble to the code we learn,

/*49  * These are the constant used to fake the fixed-point load-average50  * counting. Some notes:51  *  - 11 bit fractions expand to 22 bits by the multiplies: this gives52  *    a load-average precision of 10 bits integer + 11 bits fractional53  *  - if you want to count load-averages more often, you need more54  *    precision, or rounding will get you. With 2-second counting freq,55  *    the EXP_n values would be 1981, 2034 and 2043 if still using only56  *    11 bit fractions.57  */

These magic numbers are a result of using a fixed-point (rather than a floating-point) representation.

Using the 1 minute sampling as an example, the conversion of exp(5/60) into base-2 with 11 bits of precision occurs like this:

But EXP_M represents the inverse function exp(-5/60). Therefore, we can calculate these magic numbers directly from the formula,

where M = 1 for 1 minute sampling. Table 1 summarizes some relevant results.

Table 1: Load Average magic numbers.

These numbers are in complete agreement with those mentioned in the kernel comments above. The fixed-point representation is used presumably for efficiency reasons since these calculations are performed in kernel space rather than user space.

One question still remains, however. Where do the ratios like exp(5/60) come from?

4.2  Magic Revealed

Taking the 1-minute average as the example, CALC_LOAD is identical to the mathematical expression:

If we consider the case n = 0, eqn.(3) becomes simply:

If we iterate eqn.(4), between t = t₀ and t = T we get:

which is pure exponential decay, just as we see in Fig. 2 for times between t₀ = 2100 and t_T = 3600.

Conversely, when n = 2 as it was in our experiments, the load average is dominated by the second term such that:

which is a monotonically increasing function just like that in Fig. 2 between t₀ = 0 and t_T = 2100.

5  Summary

So, what have we learned? Those three innocuous looking numbers in the LA triplet have a surprising amount of depth behind them.

The triplet is intended to provide you with some kind of information about how much work has been done on the system in the recent past (1 minute), the past (5 minutes) and the distant past (15 minutes).

As you will have discovered if you tried the LA Triplets quiz, there are problems:

1. The "load" is not the utilization but the total queue length.
2. They are point samples of three different time series.
3. They are exponentially-damped moving averages.
4. They are in the wrong order to represent trend information.

These inherited limitations are significant if you try to use them for capacity planning purposes. I‘ll have more to say about all this in the next online column Load Average Part II: Not Your Average Average.

References

[BC01]

D. P. Bovet and M. Cesati. Understanding the Linux Kernel. O‘Reilly & Assoc. Inc., Sebastopol, California, 2001. 
 

[Coc95]

A. Cockcroft. Sun Performance and Tuning. SunSoft Press, Mountain View, California, 1^stedition, 1995. 
 

[Gun01]

N. J. Gunther. Performance and scalability models for a hypergrowth e-Commerce Web site. In R. Dumke, C. Rautenstrauch, A. Schmietendorf, and A. Scholz, editors,Performance Engineering: State of the Art and Current Trends, volume # 2047, pages 267-282. Springer-Verlag, Heidelberg, 2001. 
 

[POL97]

J. Peek, T. O‘Reilly, and M. Loukides. UNIX Power Tools. O‘Reilly & Assoc. Inc., Sebastopol, California, 2^nd edition, 1997.

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