用于云基础设施构建的自动服务器角色识别

Shinya Kitajima, Tetsuya Uchiumi, S. Kikuchi, Y. Matsumoto
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引用次数: 0

摘要

最近计算机性能的进步和虚拟化技术的发展导致了云计算的流行。提供公共云服务的数据中心提供商必须不断安装额外的资源和基础设施,以满足云用户不断增长的需求。由于新安装的基础设施(例如服务器)通常具有与现有基础设施相似的结构,因此可以复制现有基础设施的配置设置并将其用于新基础设施。一个例外是网络设置(例如IP地址),它必须为每个基础设施定制。然而,定制需要手动配置,这可能导致配置错误,从而导致新基础架构中的通信失败。识别错误配置的一种很有前途的方法是检测记录在现有基础设施和正在开发的新基础设施中的通信日志之间的差异。为了执行此方法,我们需要确定一对在现有和新的基础设施中扮演相同角色的服务器,以便我们可以验证相同的功能是否在这两个基础设施中正常工作。在本文中,我们提出了一种方法,通过检测在两个基础架构中观察到的公共通信模式来自动识别扮演相同角色的服务器对。我们在实际的云基础设施中评估了我们的方法,并确认它正确识别了94.1%的对应服务器对。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic server role identification for cloud infrastructure construction
The recent progress in computer performance and the development of virtualization technologies has led to the prevalence of cloud computing. Data center providers providing public cloud services have to install additional resources and infrastructures continuously to keep up with the increasing demands from cloud users. Since the newly installed infrastructure (e.g. servers) usually have similar structure as the existing infrastructure, the configuration settings for the existing ones can be copied and used for the new one. One of the exceptions is network setting (e.g. IP address) which must be customized for each infrastructure. However, the customization requires manual configuration, which can cause misconfigurations, resulting in communication failures in the new infrastructure. One of the promising approaches to identify the misconfigurations is to detect the differences between the communication logs recorded in the existing infrastructure and the new infrastructure being developed. In order to execute this approach, we need to identify a pair of servers that play the same role in the existing and new infrastructure so that we can verify whether or not the same functions are working properly in both of these infrastructures. In this paper, we propose a method that automatically identifies the pair of servers playing the same role by detecting the common communication patterns observed in both infrastructures. We evaluated our method in actual cloud infrastructure and confirmed that it identified 94.1% of corresponding pairs of servers correctly.
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