To manage cloud computing infrastructures consisting of many servers having a massive number of configuration parameters is quite burdensome for administrators of infrastructures. While some policy-based management approaches have been proposed to maintain the system configuration, it is quite difficult for administrators to define proper configuration policies for parameter settings in large-scale cloud computing infrastructure. To solve this problem, we developed a method of extracting parameter configuration policies from the configuration information of the existing infrastructure using UML/OCL verification. In this method, first we identify the scopes of management from the hierarchical topology of the cloud infrastructure. Next, we execute verifications of two types of OCL constraints (regarding parameter configuration patterns) for the configuration of the infrastructure, while changing the range of the scopes we focus on. By determining whether or not these constraints can be satisfied with some scopes, we extract policies that represent patterns satisfied between parameter settings of servers deployed in a certain range of the scope. Then, we demonstrate that we can derive configuration policies for all of parameters of servers in an actual cloud service infrastructure through a case study.
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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