676 research outputs found
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
Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms
Continuous-time Markov chains with alarms (ACTMCs) allow for alarm events
that can be non-exponentially distributed. Within parametric ACTMCs, the
parameters of alarm-event distributions are not given explicitly and can be
subject of parameter synthesis. An algorithm solving the -optimal
parameter synthesis problem for parametric ACTMCs with long-run average
optimization objectives is presented. Our approach is based on reduction of the
problem to finding long-run average optimal strategies in semi-Markov decision
processes (semi-MDPs) and sufficient discretization of parameter (i.e., action)
space. Since the set of actions in the discretized semi-MDP can be very large,
a straightforward approach based on explicit action-space construction fails to
solve even simple instances of the problem. The presented algorithm uses an
enhanced policy iteration on symbolic representations of the action space. The
soundness of the algorithm is established for parametric ACTMCs with
alarm-event distributions satisfying four mild assumptions that are shown to
hold for uniform, Dirac and Weibull distributions in particular, but are
satisfied for many other distributions as well. An experimental implementation
shows that the symbolic technique substantially improves the efficiency of the
synthesis algorithm and allows to solve instances of realistic size.Comment: This article is a full version of a paper accepted to the Conference
on Quantitative Evaluation of SysTems (QEST) 201
Performance evaluation of an emergency call center: tropical polynomial systems applied to timed Petri nets
We analyze a timed Petri net model of an emergency call center which
processes calls with different levels of priority. The counter variables of the
Petri net represent the cumulated number of events as a function of time. We
show that these variables are determined by a piecewise linear dynamical
system. We also prove that computing the stationary regimes of the associated
fluid dynamics reduces to solving a polynomial system over a tropical
(min-plus) semifield of germs. This leads to explicit formul{\ae} expressing
the throughput of the fluid system as a piecewise linear function of the
resources, revealing the existence of different congestion phases. Numerical
experiments show that the analysis of the fluid dynamics yields a good
approximation of the real throughput.Comment: 21 pages, 4 figures. A shorter version can be found in the
proceedings of the conference FORMATS 201
Analysis of Timed and Long-Run Objectives for Markov Automata
Markov automata (MAs) extend labelled transition systems with random delays
and probabilistic branching. Action-labelled transitions are instantaneous and
yield a distribution over states, whereas timed transitions impose a random
delay governed by an exponential distribution. MAs are thus a nondeterministic
variation of continuous-time Markov chains. MAs are compositional and are used
to provide a semantics for engineering frameworks such as (dynamic) fault
trees, (generalised) stochastic Petri nets, and the Architecture Analysis &
Design Language (AADL). This paper considers the quantitative analysis of MAs.
We consider three objectives: expected time, long-run average, and timed
(interval) reachability. Expected time objectives focus on determining the
minimal (or maximal) expected time to reach a set of states. Long-run
objectives determine the fraction of time to be in a set of states when
considering an infinite time horizon. Timed reachability objectives are about
computing the probability to reach a set of states within a given time
interval. This paper presents the foundations and details of the algorithms and
their correctness proofs. We report on several case studies conducted using a
prototypical tool implementation of the algorithms, driven by the MAPA
modelling language for efficiently generating MAs.Comment: arXiv admin note: substantial text overlap with arXiv:1305.705
A tool for model-checking Markov chains
Markov chains are widely used in the context of the performance and reliability modeling of various systems. Model checking of such chains with respect to a given (branching) temporal logic formula has been proposed for both discrete [34, 10] and continuous time settings [7, 12]. In this paper, we describe a prototype model checker for discrete and continuous-time Markov chains, the Erlangen-Twente Markov Chain Checker EĂMC2, where properties are expressed in appropriate extensions of CTL. We illustrate the general benefits of this approach and discuss the structure of the tool. Furthermore, we report on successful applications of the tool to some examples, highlighting lessons learned during the development and application of EĂMC2
- âŠ