Analytical Modeling and Simulation of Computer Systems and Networks
The course introduces the art of computer systems and network analysis. It first introduces analytical models and then support models with simulations. Further details could be found on course website.
'In doing so, it will first briefly review stochastic processes, as applied to computer system and network analyses. It will then study dependability-specific methods (fault trees, reliability block diagrams), single queues, networks of queues, and product-form analysis techniques. Finally, it will address techniques based on stochastic Petri nets and stochastic activity networks, which are appropriate for performability analysis. In doing so, it will briefly address numerical issues in the solution of both steady-state and transient models.
The simulation portion will first provide an overview of process and discrete-event-based simulation. It will then address the issues of random variable generation, and issues related to simulator execution: initial transient determination, stopping criteria, statistical issues, and design of experiments. Finally, we will discuss techniques to speed up a simulation, including variance reduction techniques.'
'In doing so, it will first briefly review stochastic processes, as applied to computer system and network analyses. It will then study dependability-specific methods (fault trees, reliability block diagrams), single queues, networks of queues, and product-form analysis techniques. Finally, it will address techniques based on stochastic Petri nets and stochastic activity networks, which are appropriate for performability analysis. In doing so, it will briefly address numerical issues in the solution of both steady-state and transient models.
ReplyDeleteThe simulation portion will first provide an overview of process and discrete-event-based simulation. It will then address the issues of random variable generation, and issues related to simulator execution: initial transient determination, stopping criteria, statistical issues, and design of experiments. Finally, we will discuss techniques to speed up a simulation, including variance reduction techniques.'