Parametric analysis is a powerful tool for designing modern embedded systems, because it permits to explore the space of design parameters, and to check the robustness of the system with respect to variations of some uncontrollable variable. In this paper, we address the problem of parametric schedulability analysis of distributed real-time systems scheduled by fixed priority. In particular, we propose two different approaches to parametric analysis: the first one is a novel technique based on classical schedulability analysis, whereas the second approach is based on model checking of Parametric Timed Automata (PTA). The proposed analytic method extends existing sensitivity analysis for single processors to the case of a distributed system, supporting preemptive and non-preemptive scheduling, jitters and unconstrained deadlines. Parametric Timed Automata are used to model all possible behaviours of a distributed system, and therefore it is a necessary and sufficient analysis. Both techniques have been implemented in two software tools, and they have been compared with classical holistic analysis on two meaningful test cases. The results show that the analytic method provides results similar to classical holistic analysis in a very efficient way, whereas the PTA approach is slower but covers the entire space of solutions.
A similar approach is followed by the SymTA S tool @cite_10 , which is based on the model @cite_11 . Another interesting approach is the Modular Performance Analysis (MPA) @cite_0 which is based on Real-Time Calculus @cite_19 . In both cases, the analysis is compositional, therefore less complex than the holistic analysis; nevertheless, these approaches are not fully parametric, in the sense that it is necessary to repeat the analysis for every combination of parameters values in order to obtain the schedulability region.
@cite_0 @cite_19 @cite_10 @cite_11
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Performance analysis plays an increasingly important role in the design of embedded real-time systems. Time-to-market pressure in this domain is high while the available implementation technology is often pushed to its limit to minimize cost. This requires analysis of performance as early as possible in the life cycle. Simulation-based techniques are often not sufficiently productive. We present an alternative, analytical, approach based on Real-Time Calculus. Modular performance analysis is presented through a case study in which several candidate architectures are evaluated for a distributed in-car radio navigation system. The analysis is efficient due to the high abstraction level of the model, which makes the technique suitable for early design exploration. SymTA S is a system-level performance and timing analysis approach based on formal scheduling analysis techniques and symbolic simulation. The tool supports heterogeneous architectures, complex task dependencies and context aware analysis. It determines system-level performance data such as end-to-end latencies, bus and processor utilisation, and worst-case scheduling scenarios. SymTA S furthermore combines optimisation algorithms with system sensitivity analysis for rapid design space exploration. The paper gives an overview of current research interests in the SymTA S project. In this paper we introduce a response time analysis technique for Synchronous Data Flow programs mapped to multiple parallel dependent tasks running on a compute cluster of the Kalray MPPA-256 many-core processor. The analysis we derive computes a set of response times and release dates that respect the constraints in the task dependency graph. We extend the Multicore Response Time Analysis (MRTA) framework by deriving a mathematical model of the multi-level bus arbitration policy used by the MPPA. Further, we refine the analysis to account for the release dates and response times of co-runners, and the use of memory banks. Further improvements to the precision of the analysis were achieved by splitting each task into two sequential phases, with the majority of the memory accesses in the first phase, and a small number of writes in the second phase. Our experimental evaluation focused on an avionics case study. Using measurements from the Kalray MPPA-256 as a basis, we show that the new analysis leads to response times that are a factor of 4.15 smaller for this application, than the default approach of assuming worst-case interference on each memory access. In this paper we present a novel performance analysis technique for large scale systems modelled in the stochastic process algebra PEPA. In contrast to the well-known approach of analysing via continuous time Markov chains, our underlying mathematical representation is a set of coupled ordinary differential equations (ODEs). This analysis process supports all of the comhinators of the PEPA algebra and is well suited to systems with large numbers of replicated components. The paper presents an elegant procedure for the generation of the ODEs and compares the results of this analysis with more conventional methods.