# Little’s Law: For Estimation Only

I had been intending on writing some posts on queuing theory for a while now, as this branch is the closest to my research interests and was the spark that sent me down the road that eventually led to my PhD dissertation. Most are quite familiar with the concepts of queuing theory, at least intuitively, so this is one of the more tangible topics in mathematics. We’ve all stood in queues at grocery stores, grouched in rush hour traffic, or had to refresh a webpage when the connection drops. I was reminded of my intentions when Datrium (to my surprise) mentioned a common queuing theory result called Little’s Law in their Storage Field Day presentation, and they even have an article they’ve written that makes use of it^{1}. What exactly is Little’s Law, and what does and doesn’t it say?

## Some queuing theory terms

To be rigorous in our discussion, we’ll need to formalize some concepts. A **queuing system** is composed of two components: a **queue **and a **server.** The queue is the line you stand in, and the server is the object performing some kind of task (like scanning your groceries). A queuing system can have more than one server^{2}, and different kinds of service policies^{3}.

### Counting the number of customers in each stage

A customer is in a queuing system when he is standing in the queue itself, and when he is being served by a server. So if we let N_{q} be the number of customers waiting in a queue, and N_{s} be the number of customers currently being served, then the total number of customers in a queuing system (N) is given by

N = N_{q} + N_{s}It’s important to note here that customers arrive randomly and are served with random **service times.** So the number of customers in a queue or in service is a random variable, and changing with time. The arrival times, the service times, and the number of customers in the queuing system all can be assigned probability distributions, but these are random variables.

### Looking at the time

When standing in a queue, you’re now in the queue; that is, you’ve arrived. Now you have to wait. How long will you wait before service? How long will service take? As anyone moving through everyday life can attest, both of those times are random. We denote W as the waiting time a customer spends in the queue before service, and X the amount of time a customer spends being served. Thus, the total is

T = W+Xand is commonly referred to as the **sojourn time**, or the total time spent in the queuing system.

Once again, W and X are random variables, and therefore so is T

### Arrival Rates

Arrivals to a queuing system are random, and governed by a counting process with a probability distribution. The most common counting process used in queuing theory is the *Poisson process* due to its very comfortable mathematical properties.

If we watched the arrivals to a queue for a very long time, a simple method of analyzing a queue is to find the **mean arrival rate**, or the average number of arrivals per unit time, typically denoted by \lambda. We could make a rough guess at it by dividing the number of arrivals in a long period of time by the length of our “watch period”. A Poisson process has a mean arrival rate, and the number of arrivals in a period of time follows a Poisson distribution.

At the risk of sounding like a broken record, I’d like to point out that a mean arrival rate of 1/hr does not mean that each hour, only one customer will arrive. It’s an *average*. Some hours will have no arrivals, some will have 5 arrivals, or 3 arrivals. An average is simply a point estimate of a typical hour.^{4}

## The Statement of Little’s Law

Little’s Law is an elegant little formula that relates the mean number of customers in a queuing system, the mean sojourn time, and the mean arrival rate. It’s quite simply written

\text{E}[N] = \lambda\text{E}[T]There’s not much to analyze here, which in many ways is nice. If you want to get the mean or expected number of customers in your queuing system at a given time, simply multiply the average arrival rate with the average time in the system.

What’s great about this law is that it’s just that–a law. It’s true regardless of the service distribution, the arrival process, or the service policies. Sounds like a silver bullet, right? Queuing theory solved!

### Some subtleties

I keep insisting the reader pay close attention to the fact that we’re dealing with random variables the whole way through. Everything is random, which means that nothing really hits the mean that often. Little’s Law is great for quick calculations and estimations, but relying too heavily on it oversimplifies a queuing system, which can be damaging. (This is similar to wantonly applying the Central Limit Theorem, even when your assumptions are not met. )

Little’s Law essentially smooths out the randomness into a deterministic, steady state equation. The means are numbers, not random variables. What we need to understand about this formula is what it doesn’t help us with. Random variables have variances, and Little’s Law just gives us an average number calculated from other averages.

## Conclusion

A queuing system is dynamic, not a steadily flowing river. Little’s Law is great in practice for estimation, and having such an elegant relationship between the means of several random variables is useful to get a general idea of what’s going on in your queuing system. Traffic planning and analysis is a difficult task because of all the randomness involved.

The danger of using Little’s Law as your silver bullet (much the way the Central Limit Theorem is commonly used as a silver bullet) is that you risk losing valuable information about the variation in the random variables that make up your queuing system, which can wreak havoc on the best-laid plans.

Example: Suppose a store owner applies Little’s Law in order to determine how many cashiers he should call in each day. He calculates via Little’s Law that he expects 10 customers/hr, and thus feels perfectly justified in only having two cashiers per day in the store. What he didn’t realize is that the weekends tended to be very busy, with swarms of people coming in the store, and Wednesdays were practically a ghost town in the store.

Everything averaged out to 10 customers/hr, but that wasn’t much help for the two cashiers who had to handle the weekend rush, and it wasn’t much help to the owner who ended up paying two cashiers that weren’t needed on Wednesdays. He probably could have just handled the store alone on Wednesdays.

The example above is a simple one, but there is an important lesson in here. Too much smoothing causes valuable information loss. Little’s Law is great for estimation, and should only be used as such.

### (Postscript)

There is a distributional version of Little’s Law, published in 1993, which is much better suited than the original Little’s Law because it discusses the relationship of the probability distributions of the random variables in the queuing system rather than simply their averages.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

#### Footnotes

- I link it here for informational purposes. I would have liked to see more in the article about how exactly they’re using it
- Like the lines at Fry’s
- First-in-first out is the most familiar, and last-in-first-out (or a stack) is another
- This is why an average is generally not that helpful. You need a variance as well to understand how spread out your arrivals are. It’s small comfort to know your mean arrival rate is 1/hr when the variance is so high you could easily get swamped with 10 arrivals all at once and find yourself or your system overwhelmed.

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