MODELS: performance MODELing of Software-defined data center networks

性能建模和预测技术为分析在现代数据中心中运行的IT系统和应用程序的性能提供了强大的工具。在本项目中，我们重点研究现代虚拟化数据中心的网络基础设施。网络虚拟化是解决当前现代网络基础设施挑战的各种方法中的主要趋势。然而，目前还没有通用的方法来建模和评估虚拟化网络的性能。现有的性能模型过于粗糙，过于集中，或者范围太有限，无法直接应用。我们提出了一种基于通用建模语言的综合建模方法，即元模型，旨在捕获数据中心网络的性能相关方面。元模型将作为使用模型到模型转换将其实例自动转换为预测模型的基础。提出的建议旨在弥合以下差距：首先，我们设想了一种性能建模方法，该方法在选择建模粒度（粗粒度分析模型与高度详细的仿真模型）方面提供了灵活性。其次，我们打算弥合软件架构、计算基础设施和网络基础设施之间的性能差距。最后，该方法将支持基于软件定义网络（SDN）作为代表性网络虚拟化技术的网络建模。拟议的项目将实现基于sdn的网络的灵活性能预测，提供用户选择的目标细节和准确性水平的结果。通过与专注于计算基础设施的现有建模技术的集成，所建议的建模方法将支持端到端数据中心基础设施（包括计算、网络和软件）的建模。此外，我们将提供第一种语言来描述基于sdn的网络设置，由于虚拟网络结构的动态变化，这种设置更难以以特别的方式进行分析。最后，我们将支持基于监测数据的网络模型自动提取，以便非专家也可以轻松地应用该方法。

Performance modeling and prediction techniques provide a powerful tool to analyze the performance of IT systems and applications running in a modern data center. In this project, we focus on the network infrastructures of modern virtualized data centers. Network virtualization is the main trend among the various approaches to address current challenges in modern network infrastructures. However, no common approach exists for modeling and evaluating the performance of virtualized networks. Existing performance models are too coarse-detailed, too focused, or too limited in scope, to be applied directly. We propose a comprehensive modeling approach based on a generic modeling language, i.e., a meta-model, designed to capture the performance-relevant aspects of data center networks. The meta-model will serve as a basis to automatically transform its instances into predictive models by using model-to-model transformations. The presented proposal aims to bridge the following gaps: First, we envision a performance modeling approach that offers flexibility in selecting the modeling granularity (coarse-grained analytical models versus highly detailed simulation models). Second, we intend to bridge the gap between the performance of the software architecture, the computing infrastructure, and the network infrastructure. Finally, the approach will support modeling of networks based on Software-Defined Networking (SDN) as a representative network virtualization technology. The proposed project will enable flexible performance prediction of SDN-based networks providing results at a user-selected targeted level of detail and accuracy. Through integration with existing modeling techniques focused on the computing infrastructure, the proposed modeling approach will support modeling of an end-to-end data center infrastructure (including computing, networking, and software). Moreover, we will provide the first language to describe SDN-based network setups which, due to the dynamic changes in the structure of virtual networks, are more difficult to analyze in an ad-hoc manner. Finally, we will support automatic extraction of network models based on monitoring data, so that the approach can be easily applied by non-experts.