Misconfiguration detection for cloud datacenters using decision tree analysis

Uchiumi, Tetsuya; Kikuchi, Shinji; Matsumoto, Yasuhide

doi:10.1109/apnoms.2012.6356072

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…Therefore, it cannot output policies representing the constraints satisfied in configuration parameters of different components (servers). Uchiumi et al proposed misconfiguration detection approach for cloud infrastructure [17]. They derive configuration patterns using decision tree analysis, but the derived rules can be wrong or inappropriate because of the probabilistic nature of decision tree.…”

Section: Related Workmentioning

confidence: 98%

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Configuration Policy Extraction for Parameter Settings in Cloud Infrastructure Using UML/OCL Verification

Kikuchi

Uchiumi

Kitajima

et al. 2013

2013 IEEE Sixth International Conference on Cloud Computing

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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.

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Section: Related Workmentioning

confidence: 98%

“…As for the formal verification methods for UML/OCL representations, several approaches have been proposed so far [17]. Cabot et al [19] proposed an algorithm based on constraint programming and Soeken et al [20] proposed a SAT-based approach.…”

Section: Related Workmentioning

confidence: 99%

Configuration Policy Extraction for Parameter Settings in Cloud Infrastructure Using UML/OCL Verification

Kikuchi

Uchiumi

Kitajima

et al. 2013

2013 IEEE Sixth International Conference on Cloud Computing

Self Cite

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“…A decision tree can generate and learn a decision-making tree-based structure from given data. In particular, for its application, it has many benefits in industrial domains, such as analysis of distribution line fault causes [24], real-time migration of fault trees to decision trees for fault detection at the International Space Station (ISS) [25], misconfiguration detection usage analysis for addressing large-scale cloud data centers [26], etc.…”

Section: Decision Tree Classifiermentioning

confidence: 99%

Identification of the Yield Rate by a Hybrid Fuzzy Control PID-Based Four-Stage Model: A Case Study of Optical Filter Industry

Chen,

Hung,

Lee

et al. 2024

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With the vigorous development of emerging technology and the advent of the Internet generation, high-speed Internet and fast transmission 5G wireless networks contribute to interpersonal communication. Now, the Internet has become popular and widely available, and human life is inseparable from various experiences on the Internet. Many base stations and data centers have been established to convert and switch from electrical transmission to optical transmission; thus, it is entering the new era of optical fiber networks and optical communication technologies. For optical communication, the manufacturing of components for the purpose of high-speed networks is a key process, and the requirement for the stability of its production conditions is very strict. In particular, product yields are always low due to the restriction of high-precision specifications associated with the limitations of too many factors. Given these reasons, this study proposes a hybrid fuzzy control-based model for industry data applications to organize advanced techniques of box-and-whisker plot method, association rule, and decision trees to find out the determinants that affect the yield rate of products and then use the fuzzy control Proportional-Integral-Derivative (PID) method to manage the determinants. Since it is unrealistic to test the real machine online operation at the manufacturing stage, the simulation software supersedes this for improved results, and a mathematical neural network is used to verify the given data to confirm whether its result is similar to that of the simulation. The study suggests that excessive temperature differentials between substrate and cavity can lead to low yields. It suggests using fuzzy control technology for temperature management, which could increase yield, reduce labor costs, and accelerate the transition to high-speed networks by mass-producing high-precision optical filters.

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“…Previously, we have proposed a misconfiguration detection method to automatically detect the candidates of misconfigurations by identifying the exceptional values of configuration parameters using statistical analysis [11]. In this method, we identify hidden patterns that comply with the majority of configuration parameters by using decision tree analysis based on the assumptions of some "uniformities" in a certain part of large-scale cloud systems.…”

Section: Introductionmentioning

confidence: 99%

Automatic server role identification for cloud infrastructure construction

Kitajima

Uchiumi

Kikuchi

et al. 2013

2013 IEEE 2nd International Conference on Cloud Networking (CloudNet)

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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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Misconfiguration detection for cloud datacenters using decision tree analysis

Cited by 10 publications

References 5 publications

Configuration Policy Extraction for Parameter Settings in Cloud Infrastructure Using UML/OCL Verification

Configuration Policy Extraction for Parameter Settings in Cloud Infrastructure Using UML/OCL Verification

Identification of the Yield Rate by a Hybrid Fuzzy Control PID-Based Four-Stage Model: A Case Study of Optical Filter Industry

Automatic server role identification for cloud infrastructure construction

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