1. DEFI Workshop, Pisa, 26-27 November, 20021 Rewarded Markov Modeling Techniques Juan A. Carraso Departament d’Enginyeria Electrònica Universitat Politècnica de Catalunya

2. DEFI Workshop, Pisa, 26-27 November, 20022 Rewarded Markov Models

3. DEFI Workshop, Pisa, 26-27 November, 20023 New applications: Grid Cluster Computing

4. DEFI Workshop, Pisa, 26-27 November, 20024 Checkpointing strategies

5. DEFI Workshop, Pisa, 26-27 November, 20025 Efficient Numerical Analysis Techniques Currently available techniques for transient analysis are expensive and are limited by CPU time consumption, not by memory consumption Currently available techniques for transient analysis are expensive and are limited by CPU time consumption, not by memory consumption For some measures (distribution of reward accumulated in a finite time interval), can only deal with tiny stiff models For some measures (distribution of reward accumulated in a finite time interval), can only deal with tiny stiff models GOAL: To develop efficient numerical analysis techniques able to deal in reasonable CPU times for as large models as possible

6. DEFI Workshop, Pisa, 26-27 November, 20026 Example: regenerative randomization and bounding regenerative randomization

7. DEFI Workshop, Pisa, 26-27 November, 20027

8. 8 Techniques to deal with large models State aggregation from suitable high-level model specification State aggregation from suitable high-level model specification Bounding methods Bounding methods “On-the-fly” model solution techniques “On-the-fly” model solution techniques GOALS: More efficient algorithms for generation of aggregated models More efficient algorithms for generation of aggregated models Smarter, more efficient bounding techniques Smarter, more efficient bounding techniques Less CPU intensive “on-the-fly” model solution techniques Less CPU intensive “on-the-fly” model solution techniques