caa a22 4500 445327227 CHVBK 20180317142724.0 cr unu---uuuuu 170323e20111201xx s 000 0 eng 10.1007/s10723-011-9194-z doi (NATIONALLICENCE)springer-10.1007/s10723-011-9194-z Towards Non-Stationary Grid Models [Elektronische Daten] [Tamás Éltető, Cécile Germain-Renaud, Pascal Bondon, Michèle Sebag] Despite intense research on Grid scheduling, differentiated quality of service remains an open question, and no consensus has emerged on the most promising strategy. The difficulties of experimentation might be one of the root causes of this stalling. An alternative to experimenting on real, large, and complex data is to look for well-founded and parsimonious representations, which may also contribute to the a-priori knowledge required for operational Autonomics. The goal of this paper is thus to explore explanatory and generative models rather than predictive ones. As a test case, we address the following issue: is it possible to exhibit and validate consistent models of the Grid workload? Most existing work on modeling the dynamics of Grid behavior describes Grids as complex systems, but assumes a steady-state system (technically stationarity) and concludes to some form of long-range dependence (slowly decaying correlation) in the associated time-series. But the physical (economic and sociologic) processes governing the Grid behavior dispel the stationarity hypothesis. This paper considers an appealing different class of models: a sequence of stationary processes separated by breakpoints. The model selection question is now defined as identifying the breakpoints and fitting the processes in each segment. Experimenting with data from the EGEE/EGI Grid, we found that a non-stationary model can consistently be identified from empirical data, and that limiting the range of models to piecewise affine (autoregressive) time series is sufficiently powerful. We propose and experiment a validation methodology that empirically addresses the current lack of theoretical results concerning the quality of the estimated model parameters. Finally, we present a bootstrapping strategy for building more robust models from the limited samples at hand. Springer Science+Business Media B.V., 2011 Modeling techniques nationallicence Traffic analysis nationallicence Model selection and validation nationallicence Éltető Tamás University Paris-Sud, INRIA-Saclay and CNRS, Orsay, France aut Germain-Renaud Cécile University Paris-Sud, INRIA-Saclay and CNRS, Orsay, France aut Bondon Pascal Laboratoire des Signaux et Systèmes, Supelec, CNRS, Gif Sur Yvette Cedex, France aut Sebag Michèle University Paris-Sud, INRIA-Saclay and CNRS, Orsay, France aut Journal of Grid Computing Springer Netherlands 9/4(2011-12-01), 423-440 1570-7873 9:4<423 2011 9 10723 https://doi.org/10.1007/s10723-011-9194-z text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10723-011-9194-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Éltető Tamás University Paris-Sud, INRIA-Saclay and CNRS, Orsay, France aut NATIONALLICENCE 700 1- Germain-Renaud Cécile University Paris-Sud, INRIA-Saclay and CNRS, Orsay, France aut NATIONALLICENCE 700 1- Bondon Pascal Laboratoire des Signaux et Systèmes, Supelec, CNRS, Gif Sur Yvette Cedex, France aut NATIONALLICENCE 700 1- Sebag Michèle University Paris-Sud, INRIA-Saclay and CNRS, Orsay, France aut NATIONALLICENCE 773 0- Journal of Grid Computing Springer Netherlands 9/4(2011-12-01), 423-440 1570-7873 9:4<423 2011 9 10723 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer