A Petri Net Tool for Software Performance Estimation Based on Upper Throughput Bounds

Functional and non-functional properties analysis (i.e., dependability, security, or performance) ensures that requirements are fulfilled during the design phase of software systems. However, the Unified Modelling Language (UML), standard de facto in industry for software systems modelling, is unsuitable for any kind of analysis but can be tailored for specific analysis purposes through profiling. For instance, the MARTE profile enables to annotate performance data within UML models that can be later transformed to formal models (e.g., Petri nets or Timed Automatas) for performance evaluation. A performance (or throughput) estimation in such models normally relies on a whole exploration of the state space, which becomes unfeasible for large systems. To overcome this issue upper throughput bounds are computed, which provide an approximation to the real system throughput with a good complexity-accuracy trade-off. This paper introduces a tool, named PeabraiN, that estimates the performance of software systems via their UML models. To do so, UML models are transformed to Petri nets where performance is estimated based on upper throughput bounds computation. PeabraiN also allows to compute other features on Petri nets, such as the computation of upper and lower marking place bounds, and to simulate using an approximate (continuous) method. We show the applicability of PeabraiN by evaluating the performance of a building closed circuit TV system

On the Performance Estimation and Resource Optimisation in Process Petri Nets

Many artificial systems can be modeled as discrete dynamic systems in which resources are shared among different tasks. The performance of such systems, which is usually a system requirement, heavily relies on the number and distribution of such resources. The goal of this paper is twofold: first, to design a technique to estimate the steady-state performance of a given system with shared resources, and second, to propose a heuristic strategy to distribute shared resources so that the system performance is enhanced as much as possible. The systems under consideration are assumed to be large systems, such as service-oriented architecture (SOA) systems, and modeled by a particular class of Petri nets (PNs) called process PNs. In order to avoid the state explosion problem inherent to discrete models, the proposed techniques make intensive use of linear programming (LP) problems

Accurate Performance Estimation for Stochastic Marked Graphs by Bottleneck Regrowing ⋆

Abstract. The adequate system performance is usually a critical requirement to be checked during the verification phase of a system. Thus, accurately measuring the performance of current industrial systems, which are often modelled as a Discrete Event Systems (DES), is a need. Due to the state explosion problem, the performance evaluation of DES becomes increasingly difficult as the size of the systems increases. Some approaches, such as the computation of performance bounds, have been developed to overcome this problem. In this paper we propose a new method to produce performance bounds that are sharper than the ones that can be achieved with current methods. The core of our method is an iterative algorithm that initially considers the most constraining bottleneck cycle of the system and adds other cycles to it in each iteration. The proposed method is deeply explained and then applied to a broad set of Marked Graphs.