FChain: Toward Black-Box Online Fault Localization for Cloud Systems

Nguyen, Hiep; Shen, Zhiming; Tan, Yongmin; Gu, Xiaohui

doi:10.1109/icdcs.2013.26

Cited by 70 publications

(43 citation statements)

References 22 publications

(21 reference statements)

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“…Such modifications are generally intrusive with significant runtime overhead. Black-box applications expose detail visibility into the application thus limiting the amount of insights achievable but they be profiled in a non-intrusive manner [60].…”

Section: Data Collectionmentioning

confidence: 99%

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Performance Anomaly Detection and Bottleneck Identification

2015

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In order to meet stringent performance requirements, system administrators must e↵ectively detect undesirable performance behaviours, identify potential root causes and take adequate corrective measures. The problem of uncovering and understanding performance anomalies and their causes (bottlenecks) in di↵erent system and application domains is well studied. In order to assess progress, research trends and identify open challenges, we have reviewed major contributions in the area and present our findings in this survey. Our approach provides an overview of anomaly detection and bottleneck identification research as it relates to the performance of computing systems. By identifying fundamental elements of the problem, we are able to categorize existing solutions based on multiple factors such as the detection goals, nature of applications and systems, system observability, and detection methods.

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Section: Data Collectionmentioning

confidence: 99%

“…Conversely, observing and understanding in-out tra c of hundreds of black-box components without prior knowledge may yield high false positive detections. [ [45]; [33]; [50]; [82]; [60] …”

Section: Flow and Dependency Analysismentioning

confidence: 99%

Performance Anomaly Detection and Bottleneck Identification

2015

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“…Tan et al presented a method of using two Bayesian classifiers for context-aware anomaly detection (one for context classification, one for anomaly detection). Aguilera et al proposed a black box performance debugging technique by analyzing message-level traces of system activities to infer causal paths [27]. Different from the work above, our work seeks to find an unsupervised method free from labeling historical data.…”

Section: Related Workmentioning

confidence: 99%

A Similarity Network Based Behavior Anomaly Detection Model for Computer Systems

Qi-jun

Cao

2014

2014 IEEE 17th International Conference on Computational Science and Engineering

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As modern computer systems become increasingly complex in infrastructure and usage, the demand for capabilities of detecting anomalous behavior has grown urgent. Although techniques for point anomaly detection have been proposed and adopted in practice, behavior anomaly detection still lacks effective approaches due to its inherent complexities. We present a new anomalous behavior detection model based on similarity network. Instead of learning behaviors according to frequencies of occurrences as current approaches do, our model exploits the similarity relationship between emerging behavior patterns. Specifically, a Markov model rank algorithm is performed on the similarity network to discover behavior anomalies. Our model is able to distinguish normal behavior patterns and anomalous ones in changing environments without training phase. We implemented this model and conducted extensive experiments on a range of data sets. Results show that our model can detect behavior anomalies in computer systems with high accuracy.

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“…Therefore, they are generally not helpful for locating faults that have occurred in a program. Previous research [13] on black-box testing proposed an online fault localization system for finding the faulty component when a program is functioning abnormally. In other words, the proposed method identifies the component causing the failure in a multicomponent system, rather than finding the exact fault location in the program.…”

Section: Testing and Debugging In A Black-box Environmentmentioning

confidence: 99%

Fault Localization Method by Partitioning Memory Using Memory Map and the Stack for Automotive ECU Software Testing

2016

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Abstract:Recently, the usage of the automotive Electronic Control Unit (ECU) and its software in cars is increasing. Therefore, as the functional complexity of such software increases, so does the likelihood of software-related faults. Therefore, it is important to ensure the reliability of ECU software in order to ensure automobile safety. For this reason, systematic testing methods are required that can guarantee software quality. However, it is difficult to locate a fault during testing with the current ECU development system because a tester performs the black-box testing using a Hardware-in-the-Loop (HiL) simulator. Consequently, developers consume a large amount of money and time for debugging because they perform debugging without any information about the location of the fault. In this paper, we propose a method for localizing the fault utilizing memory information during black-box testing. This is likely to be of use to developers who debug automotive software. In order to observe whether symbols stored in the memory have been updated, the memory is partitioned by a memory map and the stack, thus the fault candidate region is reduced. A memory map method has the advantage of being able to finely partition the memory, and the stack method can partition the memory without a memory map. We validated these methods by applying these to HiL testing of the ECU for a body control system. The preliminary results indicate that a memory map and the stack reduce the possible fault locations to 22% and 19% of the updated memory, respectively.

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FChain: Toward Black-Box Online Fault Localization for Cloud Systems

Cited by 70 publications

References 22 publications

Performance Anomaly Detection and Bottleneck Identification

Performance Anomaly Detection and Bottleneck Identification

A Similarity Network Based Behavior Anomaly Detection Model for Computer Systems

Fault Localization Method by Partitioning Memory Using Memory Map and the Stack for Automotive ECU Software Testing

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