International audienceThis paper investigates the combined use of abstraction and probabilistic learning as a means to enhance statistical model checking performance. We are given a property (or a list of properties) for verification on a (large) stochastic system. We project on a set of traces generated from the original system, and learn a (small) abstract model from the projected traces, which contain only those labels that are relevant to the property to be verified. Then, we model-check the property on the reduced, abstract model instead of the large, original system. In this paper, we propose a formal definition of the projection on traces given a property to verify. We also provide conditions ensuring the correct preservation of the property on the abstract model. We validate our approach on the Herman's Self Stabilizing protocol. Our experimental results show that (a) the size of the abstract model and the verification time are drastically reduced, and that (b) the probability of satisfaction of the property being verified is correctly estimated by statistical model checking on the abstract model with respect to the concrete system

Today, most personal mobile devices (e.g. cell phones and PDAs) are multimedia-enabled and support a variety of concurrently running applications such as audio/video players, word processors and web browsers. Media-processing applications are often computationally expensive and most of these devices typically have 100 -400 MHz processors. As a result, the user-perceived application response times are often poor when multiple applications are concurrently fired. In this paper we show that by using application-specific dynamic buffering techniques, the workload of these applications can be suitably "shaped" to fit the available processor bandwidth. Our techniques are analogous to traffic shaping which is widely used in communication networks to optimally utilize network bandwidth. Such shaping techniques have recently attracted a lot of attention in the context of embedded systems design (e.g. for dynamic voltage scaling). However, they have not been exploited for enhanced schedulability of multiple applications, as we do in this paper.

Abstract-Quality of video and audio output is a design-time constraint for portable multimedia devices. Unfortunately, there is a huge cost (e.g. buffer size) incurred to deterministically guarantee good playout quality; the worst-case workload and the timing behavior can be significantly larger than the average-case due to high variability in a multimedia system. In future mobile devices, the playout buffer size is expected to increase, so, buffer dimensioning will remain as an important problem in system design. We propose a probabilistic analytical framework that enables low-cost system design and provides bounds for playing acceptable multimedia quality. We compare our approach with a framework comprising both simulation and statistical model checking, built to simulate large embedded systems in detail. Our results show significant reduction in output buffer size compared to deterministic frameworks.