In this chapter, we propose a general framework for deriving efficient (polynomial-time) algorithms for steady-state scheduling. In the context of large scale platforms (grids or volunteer computing platforms), we show that the characteristics of the resources (volatility, heterogeneity) limit their use to large regular applications. Therefore, steady-state scheduling, that consists in optimizi...

Steady state simulation is used to study long-run behavior. Usually only the expected value of the steady state probability distribution function is estimated. In many cases quantiles of this distribution are of higher interest. In this paper a new usage of quantile estimators is proposed, which is derived from mean value analysis and is based on multiple independent replications. The advantage...

The paper presents a new modeling framework enabling to evaluate the cyclic steady state of a given system of concurrently flowing cyclic processes (SCCP) on the base of the assumed topology of transportation routes, dispatching rules employed, resources and operation times as well as an initial processes allocation. The objective is to provide the rules useful in the course of routing and sche...

This paper presents a new approach to the application mode of image processing operators, the so-called steady-state image processing. The approach reminds a steady-state genetic processing of images by considering each pixel of the image as an individual. So, some pixels are selected, processed and copied back into the image. This differs from the standard approach, where all image pixels are ...

The reverse engineering problem with probabilities and sequential behavior is introducing here, using the expression of an algorithm. The solution is partially founded, because we solve the problem only if we have a Probabilistic Sequential Network. Therefore the probabilistic structure on sequential dynamical systems is introduced here, the new model will be called Probabilistic Sequential Net...

We will discuss several techniques for obtaining point estimates and confidence intervals for steady-state performance measures. Let (X(t): t≥ 0) be a GSMP with (discrete) state space S that represents the underlying stochastic process of a simulation. Fix a real-valued function f defined on S and set Y(t) = f(X(t)) for t ≥ 0. There are several possible ways to define steady-state performance m...

Carey, DG, Tofte, C, Pliego, GJ, and Raymond, RL. Transferability of running and cycling training zones in triathletes: implications for steady-state exercise. J Strength Cond Res 23(1): 251–258, 2009—The primary objective of this study was to determine whether physiological measurements obtained from one mode of testing and training could be applied to another mode, as in prescribing heart rat...

Abstract A two-temperature linear spin model is presented that allows an easily understandable introduction to non-equilibrium statistical physics. The model is one that includes the concepts that are typical of more realistic non-equilibrium models but that allows straightforward steady state solutions and, for small systems, development of the full time dependence for configuration probabilit...

We present ASSA-PBN, a tool for approximate steady-state analysis of large probabilistic Boolean networks (PBNs). ASSA-PBN contains a constructor, a simulator, and an analyser which can approximately compute the steadystate probabilities of PBNs. For large PBNs, such approximate analysis is the only viable way to study their long-run behaviours. Experiments show that ASSAPBN can handle large PB...