通过预测性形式化分析实现异构网络的弹性

Zhijin Qin, G. Denker, C. Talcott, N. Venkatasubramanian
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引用次数: 7

摘要

近年来,无线技术的快速发展和广泛部署,带来了越来越多的业务,可以通过多种网络接入技术(如以太网、WiFi、蓝牙、LTE等)直接从移动终端访问。一个特定的流量可能会经过不同类型的网络,这大大增加了端到端连接的机会。然而,多网络的缺点是一种网络类型的故障或变化可能会影响许多流量。因此,不能孤立地管理多网络中的各种网络。相反,我们需要的方法是一致地分析这些动态和异构网络环境中变化的影响。传统的网络分析方法只关注静态网络属性,而没有充分考虑故障对跨流服务质量(QoS)的影响。在本文中,我们设计并实现了一个使用形式化方法的“假设”分析方法。我们的方法分析了异构网络中故障和变化对流的QoS的影响。形式化分析的结果可以指导网络管理员做出决策,主动调整网络配置,以实现任务或应用程序目标。我们在用例的帮助下说明了我们的方法,例如在网络中合并额外的节点或由于故障而重新配置网络。我们将我们的结果与传统的网络配置方法进行了比较,并展示了我们的形式化方法如何比传统的网络配置方法提供更有效的决策支持,并且它比模拟方法具有更好的可扩展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Achieving resilience of heterogeneous networks through predictive, formal analysis
Rapid development and wide deployment of wireless technologies in recent years have brought an increasing number and variety of services that are accessible directly from mobile terminals via multiple network access technologies (e.g, Ethernet, WiFi, Bluetooth, LTE, etc). A particular traffic flow may go through different kinds of networks, which greatly increases the end-to-end connectivity opportunities. However, the disadvantage of multinetworks is that a failure or change in one network type may affect many traffic flows. Thus, the various networks in a multinetwork cannot be managed in isolation. Rather we need methodologies that analyze the effects of changes in these dynamic and heterogeneous network environments in unison. Traditional network analysis approaches only focus on static network attributes and do not fully consider the impact of failures on quality of services (QoS) across flows. In this paper, we design and implement a "what-if" analysis methodology using formal methods. Our methodology analyzes the impact of failures and changes in heterogeneous networks on QoS of flows. The results of the formal analysis can guide network administrators in their decisions to proactively adapt network configurations to achieve mission or application objectives. We illustrate our methodology with the help of use cases such as incorporating additional nodes in a network or reconfiguring the network due to failure. We compare our results with conventional network configuration approaches and show how our formal methodology provides more effective decision support than conventional network configuration approaches and that it scales better than simulation approaches.
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