Topic
Quantitative Constraint
About: Quantitative Constraint is a research topic. Over the lifetime, 21 publications have been published within this topic receiving 179 citations.
Papers
1 Mar 2009
TL;DR: This paper proposes Quantitative Intentional Automata (QIA), an extension of CA that allow incorporating the influence of a system's environment on its performance, and shows the translation of QIA into Continuous-Time Markov Chains (CTMCs), which allows us to apply existing CTMC tools and techniques for performance analysis ofQIA and Reo circuits.
Abstract: Reo is a channel-based coordination model whose operational semantics is given by Constraint Automata (CA). Quantitative Constraint Automata extend CA (and hence, Reo) with quantitative models to capture such non-functional aspects of a system’s behaviour as delays, costs, resource needs and consumption, that depend on the internal details of the system. However, the performance of a system can crucially depend not only on its internal details, but also on how it is used in an environment, as determined for instance by the frequencies and distributions of the arrivals of I/O requests. In this paper we propose Quantitative Intentional Automata (QIA), an extension of CA that allow incorporating the influence of a system’s environment on its performance. Moreover, we show the translation of QIA into Continuous-Time Markov Chains (CTMCs), which allows us to apply existing CTMC tools and techniques for performance analysis of QIA and Reo circuits.
42 citations
11 Jun 2009
TL;DR: In this article, the authors propose Quantitative Intentional Automata (QIA), an extension of Constraint Automata that allows incorporating the influence of a system's environment on its performance.
Abstract: Reo is a channel-based coordination model whose operational semantics is given by Constraint Automata (CA). Quantitative Constraint Automata extend CA (and hence, Reo) with quantitative models to capture such non-functional aspects of a system's behaviour as delays, costs, resource needs and consumption, that depend on the internal details of the system. However, the performance of a system can crucially depend not only on its internal details, but also on how it is used in an environment, as determined for instance by the frequencies and distributions of the arrivals of I/O requests. In this paper we propose Quantitative Intentional Automata (QIA), an extension of CA that allow incorporating the influence of a system's environment on its performance. Moreover, we show the translation of QIA into Continuous-Time Markov Chains (CTMCs), which allows us to apply existing CTMC tools and techniques for performance analysis of QIA and Reo circuits.
40 citations
2 Jun 2003
TL;DR: Algorithms to automatically generate trace checkers from formulas written in the formal quantitative constraint language, Logic Of Constraints (LOC), to analyze the simulation traces for functional and performance constraint violations.
Abstract: Verification of system designs continues to be a major challenge today. Simulation remains the primary tool for making sure that implementations perform as they should. We present algorithms to automatically generate trace checkers from formulas written in the formal quantitative constraint language, Logic Of Constraints (LOC), to analyze the simulation traces for functional and performance constraint violations. For many interesting formulas, the checkers exhibit linear time complexity and constant memory usage. We illustrate the usefulness and efficiency of this approach with large designs and traces.
25 citations
TL;DR: In this article, the authors consider partially observable Markov decision processes (POMDPs) with limit-average payoff, where a reward value in the interval 0, 1 ] is associated with every transition, and the payoff of an infinite path is the long-run average of the rewards.
19 citations
TL;DR: This paper applies the integration of concerns paradigm to allow combined specification of QoS and functional properties by using Quantitative Constraint Automata, which integrate QoS aspects into service-oriented application development processes, mainly for service selection and composition.
Abstract: Assuring Quality of Service (QoS) properties is critical in Service-Oriented Application (SOA) development. In this paper, we present an approach for specifying the QoS properties of services along multiple dimensions and selecting services for their composition in a way that optimizes the QoS of the result. We apply the integration of concerns paradigm to allow combined specification of QoS and functional properties by using Quantitative Constraint Automata, which integrate QoS aspects into service-oriented application development processes, mainly for service selection and composition.
15 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2018 | 4 |
| 2015 | 3 |
| 2013 | 2 |
| 2009 | 3 |
| 2003 | 4 |
| 2000 | 2 |