System Monitoring with Metric-Correlation Models

Jiang, Miao; Munawar, Mohammad A.; Reidemeister, Thomas; Ward, Paul A. S.

doi:10.1109/tnsm.2011.120811.100033

Cited by 11 publications

(13 citation statements)

References 13 publications

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“…A common approach is to use metric correlations to quantify a monitored system. During detection, if the correlations between metrics becomes significantly different from the learned correlations, the system is classified to be in a faulty state [10,24,8,12].…”

Section: Previous Workmentioning

confidence: 99%

On the use of computational geometry to detect software faults at runtime

Stehle

Lynch

Shevertalov

et al. 2010

Proceedings of the 7th International Conference on Autonomic Computing

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Despite advances in software engineering, software faults continue to cause system downtime. Software faults are difficult to detect before the system fails, especially since the first symptom of a fault is often system failure itself.This paper presents a computational geometry technique and a supporting tool to tackle the problem of timely fault detection during the execution of a software application. The approach involves collecting a variety of runtime measurements and building a geometric enclosure, such as a convex hull, which represents the normal (i.e., non-failing) operating space of the application being monitored. When collected runtime measurements are classified as being outside of the enclosure, the application is considered to be in an anomalous (i.e., failing) state. This paper presents experimental results that illustrate the advantages of using a computational geometry approach over the distance based approaches of Chi-Squared and Mahalanobis distance. Additionally, we present results illustrating the advantages of using the convex-hull enclosure for fault detection in favor of a simpler enclosure such as a hyperrectangle

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Section: Previous Workmentioning

confidence: 99%

On the use of computational geometry to detect software faults at runtime

Stehle

Lynch

Shevertalov

et al. 2010

Proceedings of the 7th International Conference on Autonomic Computing

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“…Various researchers (e.g., Jiang et al [1], deGrandis and Valetto [2], Archarya and Kommineni [3], Stehle et al [4], Burgess et al [8], Garlan et al [9]) have studied the use of utility functions in detecting software faults. For example, Archarya and Kommineni [3] proposed an approach for 1 http://www.w3.org/Jigsaw/ filtering redundant and irrelevant threshold values, and use domain experts to manually remove the remaining irrelevant resource usage metrics.…”

Section: A Utility Functionsmentioning

confidence: 99%

“…For example, Archarya and Kommineni [3] proposed an approach for 1 http://www.w3.org/Jigsaw/ filtering redundant and irrelevant threshold values, and use domain experts to manually remove the remaining irrelevant resource usage metrics. Jiang et al [1] show that using metriccorrelation models, such as the generalized least squares regression, can lead to better detection of errors than the current linear metric models. Principal component analysis [10] has also been applied to identify the most relevant resource metrics for detecting software faults.…”

Section: A Utility Functionsmentioning

confidence: 99%

“…Recent published research [1]- [4] describes the promise of using utility functions to detect software faults and anomalous behavior at runtime. These functions often are based on various resource usage metrics (e.g., memory usage, processor usage, thread count) and are often derived by imposing a lower and an upper threshold on the values of each metric.…”

Section: Introductionmentioning

confidence: 99%

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Reverse Engineering Utility Functions Using Genetic Programming to Detect Anomalous Behavior in Software

Wong

Aaron

Segall

et al. 2010

2010 17th Working Conference on Reverse Engineering

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Recent studies have shown the promise of using utility functions to detect anomalous behavior in software systems at runtime. However, it remains a challenge for software engineers to hand-craft a utility function that achieves both a high precision (i.e., few false alarms) and a high recall (i.e., few undetected faults). This paper describes a technique that uses genetic programming to automatically evolve a utility function for a specific system, set of resource usage metrics, and precision/recall preference. These metrics are computed using sensor values that monitor a variety of system resources (e.g., memory usage, processor usage, thread count). The technique allows users to specify the relative importance of precision and recall, and builds a utility function to meet those requirements. We evaluated the technique on the open source Jigsaw web server using ten resource usage metrics and five anomalous behaviors in the form of injected faults in the Jigsaw code and a security attack. To assess the effectiveness of the technique, the precision and recall of the evolved utility function was compared to that of a hand-crafted utility function that uses a simple thresholding scheme. The results show that the evolved function outperformed the hand-crafted function by 10 percent.

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“…The diagnostic approaches differ by monitoring data and overhead. To localize abnormal components from application traces we refer the reader to [16], for identifying recurrent faults from log-files to [13], [14], and finally for localizing faulty components the reader is referred to [15], [17]- [19].…”

Section: Incorporating Symptoms Of Known Faultsmentioning

confidence: 99%

An extensible framework for repair-driven monitoring

Reidemeister

Jiang

Ward

2010

2010 International Conference on Network and Service Management

Self Cite

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In recent years autonomic computing, specifically autonomic data centre management has gained significant attention. Human intervention be minimized to reduce the operating costs of business applications. In this paper we focus our attention to the self-repair dimension and present a flexible probabilistic framework to develop agents for self-repair in the context of business-information-system components. Our framework seeks to pick the optimal sequence of repair actions given only imperfect information about the experienced fault. In contrast to existing recovery-oriented approaches, our model explicitly considers fault prevalence, symptoms of recurrent failures, and inclusive repair actions.We evaluate our proposal using discrete event simulation. Our evaluation shows that an optimal repair policy can be computed from a brief specification of repair actions. Even in the context of very unreliable error detection our controller is able to estimate the current state of the monitored system and recover from failure.

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System Monitoring with Metric-Correlation Models

Cited by 11 publications

References 13 publications

On the use of computational geometry to detect software faults at runtime

On the use of computational geometry to detect software faults at runtime

Reverse Engineering Utility Functions Using Genetic Programming to Detect Anomalous Behavior in Software

An extensible framework for repair-driven monitoring

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