Ranking the importance of alerts for problem determination in large computer systems

Jiang, Guofei; Chen, Haifeng; Yoshihira, Kenji; Saxena, Akhilesh

doi:10.1007/s10586-010-0120-0

Cited by 23 publications

(6 citation statements)

References 10 publications

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“…Correlation-based techniques can be used to analyze data and automatically discover causal relationships among pairs of metrics [24], [77]- [79]. Perturbation in the learned correlations may indicate faults.…”

Section: A Statistical Techniquesmentioning

confidence: 99%

Recent Advances in Fault Localization in Computer Networks

Dusia

Sethi

2016

IEEE Commun. Surv. Tutorials

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Fault localization, a core element in network fault management, is the process of inferring the exact failure in a network from the set of observed symptoms. Since faults in network systems can be unavoidable, their quick and accurate detection and diagnosis is important for the stability, consistency, and performance of a communication system. In this paper, we discuss the challenges of fault localization in complex communication systems and present an overview of recent techniques proposed in the literature along with their advantages and limitations. We start by briefly surveying passive monitoring techniques which were previously reviewed in a survey by Steinder [1]. We then describe more recent fault localization research in five categories: active monitoring techniques, techniques for overlay and virtual networks, decentralized probabilistic management techniques, temporal correlation techniques, and learning techniques.

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Section: A Statistical Techniquesmentioning

confidence: 99%

Recent Advances in Fault Localization in Computer Networks

Dusia

Sethi

2016

IEEE Commun. Surv. Tutorials

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“…Their correlation-like "rules" for inferring system problems are not general, unlike the LFD hypothesis. Some diagnostic approaches have used regression to automatically discover correlations between metric pairs [10,11]. However, they do not scale well to large numbers of nodes/metrics as they search for metric correlations locally and remotely between nodes.…”

Section: Related Workmentioning

confidence: 99%

Light-weight black-box failure detection for distributed systems

Tan

Kavulya

Gandhi

et al. 2012

Proceedings of the 2012 Workshop on Management of Big Data Systems

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Detecting failures in distributed systems is challenging, as modern datacenters run a variety of applications. Current techniques for detecting failures often require training, have limited scalability, or have results that are hard to interpret. We present LFD, a light-weight technique to quickly detect performance problems in distributed systems using only correlations of OS metrics. LFD is based on our hypothesis of server application behavior, does not require training, and detects failures with complexity linear in the number of nodes, with results that are interpretable by sysadmins. We further show that LFD is versatile, and can diagnose faults in Hadoop MapReduce systems and on multi-tier web request systems, and show how LFD is intuitive to sysadmins.

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“…Several other papers [5,11] took similar statistical analysis approaches to diagnose problems from system monitoring data. Jiang et al [14] developed an invariant-based approach to profile large systems for system management. But none of these works require real-time guarantee in diagnosis.…”

Section: Related Workmentioning

confidence: 99%

Session management of correlated multi-stream 3D tele-immersive environments

Arefin

2011

Proceedings of the 19th ACM International Conference on Multimedia

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Quality control and resource optimization are challenging problems in 3D tele-immersive (3DTI) environments due to their large scale, multi-stream dependencies and dynamic peer (viewer) behavior. Such systems are also prone to performance degradation due to undesired behavior in the event of drastic demand changes, such as view change and large-scale simultaneous viewer arrivals or departures. Therefore, it is crucial to localize undesired behavior inside the system and re-organize the streaming overlay structures accordingly. Doing this accurately for a large scale is even more challenging and it requires to capture all events effecting the data plan and control plan of the system. Moreover, to do this, we need to understand the desired behavior of the application first, which is defined by the dependency patterns of performance and configuration metadata at each participating peers. To assist that, we propose a learning framework that discovers metadata dependency patterns from the time series metadata and uses an online profiler to detect undesired behavior of the system during run-time. Such universal protocol also enables the prediction of large scale performance degradation due to irregular dependencies. Finally an adaptation is proposed that reallocates the resources and rearranges overlay structures to overcome the undesired behavior. In summary, our goal is to provide a universal session monitoring and management framework for complex multi-stream 3DTI environments to support large number of concurrent viewers. We consider the difficulty in overlay construction, collecting metadata, answering queries, learning patterns, detecting undesired behavior at the participating peers and finally overlay adaptation considering multi-stream dependencies.

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Ranking the importance of alerts for problem determination in large computer systems

Cited by 23 publications

References 10 publications

Recent Advances in Fault Localization in Computer Networks

Recent Advances in Fault Localization in Computer Networks

Light-weight black-box failure detection for distributed systems

Session management of correlated multi-stream 3D tele-immersive environments

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