|Speaker :||Alexandre Brandwajn|
|University of California at Santa Cruz|
|Time:||11:00 am - 12:00 pm|
|Location:||LINCS Meeting Room 40|
Queueing network models are an important tool in the evaluation of the performance of computer systems and networks. Explicit analytical solutions exist for a class of such models, but features such as realistic global dependencies, priorities, or simple commonly used service disciplines, preclude their direct application. Additionally, even when such solutions are known, their numerical computation may still be challenging due to the size of the state space of classical queueing models.
In this talk, we try to show that the use of conditional probabilities may be valuable in exposing simple properties hidden from view by classical state descriptions. Examples include tandem networks with blocking, multiclass models, multi-server systems with priorities, as well as guided state sampling in large systems.