SOF:基于云的零配置仿真优化框架

M. Carillo, G. Cordasco, Flavio Serrapica, V. Scarano, Carmine Spagnuolo, Przemysław Szufel
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引用次数: 5

摘要

仿真模型已成为分析和优化复杂实际系统的一种日益流行的工具。找到最优的系统设计需要执行大参数扫描。本文提出了利用云计算环境的计算能力来实现有效、高效的仿真优化策略的框架SOF (Simulation Optimization and exploration Framework on the cloud)的设计。soff提供了几个有吸引力的特性:soff需要“零配置”,因为它不需要在远程节点上安装任何额外的软件;soff对用户是透明的,因为用户完全不知道系统在分布式环境中运行;soff是高度可定制和可编程的,因为它允许在不同的仿真工具包上运行不同的仿真优化场景。该工具已经完全开发,并且可以在Apache公共许可下的公共存储库中使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SOF: Zero Configuration Simulation Optimization Framework on the Cloud
Simulation models are becoming an increasingly popular tool for the analysis and optimization of complex real systems in different fields. Finding an optimal system design requires performing a large parameter sweep. In this paper, we present the design of SOF (Simulation Optimization and exploration Framework on the cloud), a framework which exploits the computing power of a cloud computational environment in order to realize effective and efficient simulation optimization strategies. SOF offers several attractive features: SOF requires "zero configuration" as it does not require any additional software installed on the remote node, SOF is transparent to the user, since the user is totally unaware that system operates on a distributed environment, SOF is highly customizable and programmable, since it enables the running of different simulation optimization scenarios on different simulation toolkits. The tool has been fully developed and is available on a public repository under the Apache public licence.
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