# Simulation and Performance Evaluation

Performance Evaluation (PE for short) is central to all systems design, and ICT systems are no exception. Event driven simulations of just Simulations to be brief, are one of the means to evaluate performance, but since very often the term "simulation" is used with different meanings, and since it is unfortunately widespread to think about simulations in bery different terms, we explicit it in the title that Simulation techniques and methodology are part of this course.

The main objective of the course is defining what PE means, what are the applications areas, and why it is (or should be) a centerpiece of any system design ... including computers, software, the Internet, and applications in general. Very often performance is just seen as speed ("how fast is your system"?), but it is not a great intellectual feat to understand that speed alone is not sufficient: even a racing car can be the fastest of the lot, but if it breaks down it does not win the race! So reliability is part of performance, availability too, and dependability even more, specially for mission-critical systems.

One of the major issues we will have to clarify is that performance is rarely (if ever) a simple number, but it has to be expressed in stochastic terms, and often as complex vectors of time-dependent variables, and this explain why the starting point of our course is some reasonable knowledge of Probability and Statistics (which in turn imply some reasonable knowledge of Analysis). And we give for granted that, as you all are graduate students of Computer Science, you are all much better than me in programming techniques and software engineering, so that the implementation of simple algorthms, drawting some graphs and manipulating matrices and programs outputs is not a problem.

We will try to make a logical path that starts froms systems, simple or complex as they are, understand, given the system, what are the relevant metrics that define their perfromance, and what are the dependencies between different metrics and different subsystems. Once we have understood what are the metrics of interest and their relationships, we have to take a tough decision to evaluate performance: shall we measure them on the real system, simulate the system, or build an analytical model?

We will start from measures, or better, from the interpretation of measure points and from the design of a correct experimental setup do do the measures. In some cases we might use a simulator as if it were a real system, because this gives us more flexibility ... and makes experiments much, much cheaper!

Next we will introduce simulations, understand what they really are from a mathematical point of view, and learn the basics to design a simulator.

Finally we will make some examples of systems that are prone to an analytical modeling and how these models can be solved to yield results that are otherwise impossible to obtain.

Type:
Master