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ModelGraft: Accurate, Scalable, and Flexible Performance Evaluation of General Cache Networks

机译：ModelGraft：通用缓存网络的准确，可扩展和灵活的性能评估

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Large scale deployments of general cache networks, such as Content Delivery Networks or Information Centric Networking architectures, arise new challenges regarding their performance evaluation for network planning. On the one hand, analytical models can hardly represent in details all the interactions of complex replacement, replication, and routing policies on arbitrary topologies. On the other hand, the sheer size of networks and content catalogs makes event-driven simulation techniques inherently non-scalable. We propose a new technique for the performance evaluation of large-scale caching systems that intelligently integrates elements of stochastic analysis within a MonteCarlo simulative approach, that we colloquially refer to as ModelGraft. Our approach (i) leverages the intuition that complex scenarios can be mapped to a simpler equivalent scenario that builds upon Time-To-Live (TTL) caches, it (ii) significantly downscales the scenario to lower computation and memory complexity, while, at the same time, preserving its properties to limit accuracy loss, finally, it (iii) is simple to use and robust, as it autonomously converges to a consistent state through a feedback-loop control system, regardless of the initial state. Performance evaluation shows that, with respect to classic event-driven simulation, ModelGraft gains over two orders of magnitude in both CPU time and memory complexity, while limiting accuracy loss below 2%. In addition, we show that ModelGraft extends performance evaluation well beyond the boundaries of classic approaches, by enabling study of Internet-scale scenarios with content catalogs comprising hundreds of billions objects.
机译：诸如内容交付网络或信息中心网络体系结构之类的通用缓存网络的大规模部署，在其对网络规划的性能评估方面提出了新的挑战。一方面，分析模型几乎无法详细表示复杂替换，复制和路由策略在任意拓扑上的所有交互。另一方面，网络和内容目录的庞大规模使事件驱动的仿真技术固有地不可扩展。我们提出了一种用于大规模缓存系统性能评估的新技术，该技术可以智能地将随机分析的元素整合到MonteCarlo仿真方法中，我们通称为ModelGraft。我们的方法（i）充分利用了直觉，即可以将复杂方案映射到基于生存时间（TTL）缓存的更简单的等效方案，它（ii）大大降低了方案的规模，从而降低了计算和内存的复杂性，同时同时，保留其特性以限制精度损失，最后，它（iii）使用简单且坚固耐用，因为它通过反馈回路控制系统自动收敛到一致状态，而与初始状态无关。性能评估表明，相对于经典的事件驱动模拟，ModelGraft在CPU时间和内存复杂性方面都获得了两个数量级的提升，同时将精度损失限制在2％以下。此外，通过支持对包含数千亿个对象的内容目录的Internet规模场景的研究，我们证明了ModelGraft大大超越了传统方法的性能评估范围。

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