Software Performance and Scalability: A Quantitative Approach

Part 2. Applying Queuing Theory

	Mathematics is the language with which God has written the universe.
	--Galileo Galilei, 1564–1642

	No human investigation can be called real science if it cannot be demonstrated mathematically.
	--Leonardo da Vinci

Coping with the performance and scalability of a software system effectively depends not only on the quantitative measurements as described in Part I but also on the quantitative analysis based on proven theories such as queuing theory. It's more reliable to count on queuing theory than on gut instincts in identifying bottlenecks that limit the performance and scalability of a software system, since queuing theory can provide more quantitative and objective guidance. Queuing theory is helpful not only for arriving at the conclusions about where the bottlenecks are but also for giving the prescriptions about how the bottlenecks can actually be removed.

This part is dedicated to queuing theory augmented with case studies as follows:

• Chapter 4—Introduction to Queuing Theory

• Chapter 5—Case Study I: Queuing Theory Applied to SOA

• Chapter 6—Case Study II: Queuing Theory Applied to Optimizing and Tuning Software Performance and Scalability

Please note that the material presented in this part is not an introduction to queuing theory in general and in depth. I'd like to help you learn the part of queuing theory that is most relevant in the context of the performance and scalability of a software system. In other words, queuing theory is introduced in ...