Efficient synthesis of robust models for stochastic systems
Efficient synthesis of robust models for stochastic systems
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DOI：
10.1016/j.jss.2018.05.013
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发表时间：
2018-09-01
影响因子：
3.5
通讯作者：
Paoletti, Nicola
中科院分区：
文献类型：
--
作者：
Calinescu, Radu;Ceska, Milan;Paoletti, Nicola
我们描述了一种工具支持的方法，用于有效合成参数连续时间马尔可夫链（pCTMC），该方法对应于正在开发的系统的稳健设计。由我们的 RObust DEsign Synthesis (RODES) 方法生成的 pCTMC 可适应系统运行情况的变化，满足严格的可靠性、性能和其他质量约束，并且就一组质量优化标准而言是帕累托最优或接近帕累托最优。通过在设计者指定的公差水平和帕累托最优性下集成灵敏度分析，RODES 生成的设计可能稍微次优，但灵敏度较低，这是工程实践中可接受的权衡。通过使用 RODES 设计生产者-消费者系统、复制文件系统和工作站集群系统，我们展示了我们的方法的有效性及其跨多个应用领域的 GPU 加速工具支持的效率。
We describe a tool-supported method for the efficient synthesis of parametric continuous-time Markov chains (pCTMC) that correspond to robust designs of a system under development. The pCTMCs generated by our RObust DEsign Synthesis (RODES) method are resilient to changes in the system's operational profile, satisfy strict reliability, performance and other quality constraints, and are Pareto-optimal or nearly Pareto-optimal with respect to a set of quality optimisation criteria. By integrating sensitivity analysis at designer-specified tolerance levels and Pareto optimality, RODES produces designs that are potentially slightly suboptimal in return for less sensitivity-an acceptable trade-off in engineering practice. We demonstrate the effectiveness of our method and the efficiency of its GPU-accelerated tool support across multiple application domains by using RODES to design a producer-consumer system, a replicated file system and a workstation cluster system.