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Exact-Differential Simulation: Differential Processing of Large-Scale Discrete Event Simulations

机译:精确差分仿真:大规模离散事件仿真的差分处理

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Using computer simulation to analyze large-scale discrete event systems requires repeated executions with various scenarios or parameters. Such repeated executions can induce significant redundancy in event processing when the modification from a prior scenario to a new scenario is relatively minor, and when the altered scenario influences only a small part of the simulation. For example, in a city-scale traffic simulation, an altered scenario of blocking one junction may only affect a small part of the city for considerable length of time. However, traditional simulation approaches would still repeat the simulation for the whole city even when the changes are minor. In this article, we propose a new redundancy reduction technique for large-scale discrete event simulations, called exact-differential simulation, which simulates only the altered portions of scenarios and their influences in repeated executions while still achieving the same results as the re-execution of entire simulations. This article presents the main concepts of the exact-differential simulation, the design of its algorithm, and an approach to build an exact-differential simulation middleware that supports multiple applications of discrete event simulation. We also evaluate our approach by using two case studies, PHOLD benchmark and a traffic simulation of Tokyo.
机译:使用计算机仿真来分析大规模离散事件系统需要使用各种方案或参数重复执行。当从先前场景到新场景的修改相对较小时,并且当更改后的场景仅影响模拟的一小部分时,此类重复执行会在事件处理中引起大量的冗余。例如,在城市规模的交通模拟中,更改一个路口的场景可能只会在相当长的时间内影响一小部分城市。但是,即使变化很小,传统的模拟方法仍将对整个城市重复进行模拟。在本文中,我们提出了一种用于大规模离散事件模拟的新冗余减少技术,称为精确差分模拟,该技术仅模拟方案的变更部分及其在重复执行中的影响,同时仍获得与重新执行相同的结果整个模拟。本文介绍了精确差分仿真的主要概念,其算法的设计以及一种构建精确差分仿真中间件的方法,该中间件支持离散事件仿真的多种应用。我们还通过使用两个案例研究,PHOLD基准测试和东京的交通模拟来评估我们的方法。

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