Tag Archives: Mean Field models

06/05/2015: Talk by Benny Van Houdt (University of Antwerp)

Title: Mean field models for SSD garbage collection
Time: 14:00
Location: Conference Room, building Alpha
Type: Research result
Speaker: Benny Van Houdt
Abstract: In this talk we discuss some mean field models for a broad class of garbage collection algorithms for flash-based solid state drives (SSDs) and as well as the insights they provide. We start with a basic introduction on mean field models and SSDs. Next we take a detailed look at the mean field model in the most basic setting (uniform random writes) and discuss its implications. Finally, if time permits, some of the new insights provided by more advanced models with hot and cold data or hot data identification will be presented.

29.05.2013 – Talk by Luca Bortolussi

Title: Mean field approximation for stochastic model checking
Time: 14:00
Location: Meeting room
Type: Research Result
Speaker: Luca Bortolussi
Mean field approximation techniques are well established approaches to analyse large-scale stochastic processes, especially population Markov models. Some years ago, they have entered the arena of quantitative formal methods, and they provided a consistent way to define fluid semantics for stochastic process algebras.
Here, we will discuss how mean field approximation can play a role in another successful area of quantitative formal methods, namely stochastic model checking. In particular, we will focus on a subclass of Continuous Stochastic Logic (CSL), considering formulae that expresses properties of single agents in a population model, and we will present an approximate model checking algorithm based on mean field approximation. We will also discuss model checking CSL formulae against time-inhomogeneous CTMC models, as this turns out to be the core procedure needed for fluid approximation.
Finally, we will consider a class of global properties that can be analysed by linear noise approximation, a higher order fluid approximation.