ReasoNet:使用本体推断网络策略

Charalampos Rotsos, Arsham Farshad, D. King, D. Hutchison, Qianru Zhou, A. Gray, Chengxiang Wang, S. Mclaughlin
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引用次数: 5

摘要

现代软件定义网络(SDN)控制栈由多个抽象层和虚拟化层组成,可以灵活地开发新的控制特性。在跨控制层共享信息时,富数据建模框架是必不可少的。不幸的是,现有的Network Operating System (NOS)数据建模功能仅限于简单的类型检查和代码模板。我们对SDN数据建模的一个更极端的点进行了探索:ReasoNet。开发人员可以使用语义web技术通过推理规则和完整性/一致性约束来丰富他们的数据模型,并自动跨层进行状态推断。我们通过实现两个流行的控制应用程序,一个学习开关和一个服务质量(QoS)策略引擎,展示了ReasoNet自动状态验证和跨层调试的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ReasoNet: Inferring Network Policies Using Ontologies
Modern Software Defined Networking (SDN) control stacks consist of multiple abstraction and virtualization layers to enable flexibility in the development of new control features. Rich data modeling frameworks are essential when sharing information across control layers. Unfortunately, existing Network Operating System (NOS) data modeling capabilities are limited to simple type-checking and code templating. We present an exploration of a more extreme point on SDN data modeling: ReasoNet. Developers can use semantic web technologies to enrich their data models with reasoning rules and integrity/consistency constraints, and automate state inference across layers. We demonstrate the ability of ReasoNet to automate state verification and cross-layer debugging, through the implementation of two popular control applications, a learning switch and a Quality of Service (QoS) policy engine.
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