Part III is about operational laws. Operational laws are very powerful because they apply to any system or part of a system. They are both simple and exact. A very important feature of operational laws is that they are “distribution independent.” This means, for example, that the laws do not depend on the distribution of the job service requirements (job sizes), just on their mean. Likewise the results do not depend on the distribution of the job interarrival times, just on the mean arrival rate. The fact that the results do not require the Markovian assumptions that we will see in Part IV, coupled with the fact that using operational laws is so easy, makes these laws very popular with system builders.

The most important operational law that we will study is Little's Law, which relates the mean number of jobs in any system to the mean response time experienced by arrivals to that system. We will study Little's Law and several other operational laws in Chapter 6.

In Chapter 7 we will see how to put together several operational laws to prove asymptotic bounds on system behavior (specifically, mean response time and throughput) for closed systems. Asymptotic bounds will be proven both in the limit as the multiprogramming level approaches infinity and in the limit as the multiprogramming level approaches 1. These asymptotic bounds will be very useful in allowing us to answer “what-if” questions of the form, “Is it preferable to increase the speed of the CPU by a factor of 2, or to increase the speed of the I/O device by a factor of 3, or does neither really make a difference?”