Examining Load Average
Many Linux administrators and support technicians regularly use the top utility for real-time monitoring of their system state. In some shops, it is very typical to check top first when there is any sign of trouble. In that case, top becomes the de facto critical measurement of the machine's health. If top looks good, there must not be any system problems. top is rich with information—memory usage, kernel states, process priorities, process owner and so forth all can be obtained from top. But, what is the purpose of those three curious load averages, and what exactly are they trying to tell me? To answer those questions, an intuitive as well as a detailed understanding of how the values are formed are necessary. Let's start with intuition.
The three load-average values in the first line of top output are the 1-minute, 5-minute and 15-minute average. (These values also are displayed by other commands, such as uptime, not only top.) That means, reading from left to right, one can examine the aging trend and/or duration of the particular system state. The state in question is CPU load—not to be confused with CPU percentage. In fact, it is precisely the CPU load that is measured, because load averages do not include any processes or threads waiting on I/O, networking, databases or anything else not demanding the CPU. It narrowly focuses on what is actively demanding CPU time. This differs greatly from the CPU percentage. The CPU percentage is the amount of a time interval (that is, the sampling interval) that the system's processes were found to be active on the CPU. If top reports that your program is taking 45% CPU, 45% of the samples taken by top found your process active on the CPU. The rest of the time your application was in a wait. (It is important to remember that a CPU is a discrete state machine. It really can be at only 100%, executing an instruction, or at 0%, waiting for something to do. There is no such thing as using 45% of a CPU. The CPU percentage is a function of time.) However, it is likely that your application's rest periods include waiting to be dispatched on a CPU and not on external devices. That part of the wait percentage is then very relevant to understanding your overall CPU usage pattern.
The load averages differ from CPU percentage in two significant ways: 1) load averages measure the trend in CPU utilization not only an instantaneous snapshot, as does percentage, and 2) load averages include all demand for the CPU not only how much was active at the time of measurement.
Authors tend to overuse analogies and sometimes run the risk of either insulting the reader's intelligence or oversimplifying the topic to the point of losing important details. However, freeway traffic patterns are a perfect analogy for this topic, because this model encapsulates the essence of resource contention and is also the chosen metaphor by many authors of queuing theory books. Not surprisingly, CPU contention is a queuing theory problem, and the concepts of arrival rates, Poisson theory and service rates all apply. A four-processor machine can be visualized as a four-lane freeway. Each lane provides the path on which instructions can execute. A vehicle can represent those instructions. Additionally, there are vehicles on the entrance lanes ready to travel down the freeway, and the four lanes either are ready to accommodate that demand or they're not. If all freeway lanes are jammed, the cars entering have to wait for an opening. If we now apply the CPU percentage and CPU load-average measurements to this situation, percentage examines the relative amount of time each vehicle was found occupying a freeway lane, which inherently ignores the pent-up demand for the freeway—that is, the cars lined up on the entrances. So, for example, vehicle license XYZ 123 was found on the freeway 30% of the sampling time. Vehicle license ABC 987 was found on the freeway 14% of the time. That gives a picture of how each vehicle is utilizing the freeway, but it does not indicate demand for the freeway.
Moreover, the percentage of time these vehicles are found on the freeway tells us nothing about the overall traffic pattern except, perhaps, that they are taking longer to get to their destination than they would like. Thus, we probably would suspect some sort of a jam, but the CPU percentage would not tell us for sure. The load averages, on the other hand, would.
This brings us to the point. It is the overall traffic pattern of the freeway itself that gives us the best picture of the traffic situation, not merely how often cars are found occupying lanes. The load average gives us that view because it includes the cars that are queuing up to get on the freeway. It could be the case that it is a nonrush-hour time of day, and there is little demand for the freeway, but there just happens to be a lot of cars on the road. The CPU percentage shows us how much the cars are using the freeway, but the load averages show us the whole picture, including pent-up demand. Even more interesting, the more recent that pent-up demand is, the more the load-average value reflects it.
Taking the discussion back to the machinery at hand, the load averages tell us by increasing duration whether our physical CPUs are over- or under-utilized. The point of perfect utilization, meaning that the CPUs are always busy and, yet, no process ever waits for one, is the average matching the number of CPUs. If there are four CPUs on a machine and the reported one-minute load average is 4.00, the machine has been utilizing its processors perfectly for the last 60 seconds. This understanding can be extrapolated to the 5- and 15-minute averages.
In general, the intuitive idea of load averages is the higher they rise above the number of processors, the more demand there is for the CPUs, and the lower they fall below the number of processors, the more untapped CPU capacity there is. But all is not as it appears.
|PasswordPing Ltd.'s Exposed Password and Credentials API Service||Apr 28, 2017|
|Graph Any Data with Cacti!||Apr 27, 2017|
|Be Kind, Buffer!||Apr 26, 2017|
|Preparing Data for Machine Learning||Apr 25, 2017|
|openHAB||Apr 24, 2017|
|Omesh Tickoo and Ravi Iyer's Making Sense of Sensors (Apress)||Apr 21, 2017|
- Graph Any Data with Cacti!
- Teradici's Cloud Access Platform: "Plug & Play" Cloud for the Enterprise
- The Weather Outside Is Frightful (Or Is It?)
- Understanding Firewalld in Multi-Zone Configurations
- A Switch for Your RPi
- Simple Server Hardening
- Gordon H. Williams' Making Things Smart (Maker Media, Inc.)
- Buddy Platform Limited's Parse on Buddy Service
- From vs. to + for Microsoft and Linux
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