Towards an analysis framework for tasks with probabilistic execution times and probabilistic inter-arrival times

In this paper we investigate the problem of calculating the response time distribution for real-time tasks with probabilistic worst-case execution times, probabilistic inter-arrival times and probabilistic deadlines. We propose a definition for the probabilistic deadlines and a first discussion on the response time calculation

Study of the Reliability of Statistical Timing Analysis for Real-Time Systems

Presented at 23rd International Conference on Real-Time Networks and Systems (RTNS 2015). 4 to 6, Nov, 2015, Main Track. Lille, France.Probabilistic and statistical temporal analyses have been developedas a means of determining the worst-case execution and responsetimes of real-time software for decades. A number of such methodshave been proposed in the literature, of which the majority claim tobe able to provide worst-case timing scenarios with respect to agiven likelihood of a certain value being exceeded. Further, suchclaims are based on either some estimates associated with a probability,or probability distributions with a certain level of confidence.However, the validity of the claims are very much dependent on anumber of factors, such as the achieved samples and the adopteddistributions for analysis.In this paper, we investigate whether the claims made are in facttrue as well as the establishing an understanding of the factors thataffect the validity of these claims. The results are of importancefor two reasons: to allow researchers to examine whether there areimportant issues that mean their techniques need to be refined; andso that practitioners, including industrialists who are currently usingcommercial timing analysis tools based on these types of techniques,understand how the techniques should be used to ensure theresults are fit for their purposes

An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System

Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for dynamically selecting the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to take tuning decisions on the number of samples improving the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36% and 81%) with respect to a static approach while considering different traffic situations, paths and error requirements. Given the negligible runtime overhead of the proposed approach, it results in an execution-time speedup between 1.5x and 5.1x. This speedup is reflected at infrastructure-level in terms of a reduction of around 36% of the computing resources needed to support the whole navigation pipeline

Stochastic Modeling and Performance Analysis of Multimedia SoCs

International audienceQuality 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