Module Prototype for Online Failure Prediction for the IBM Blue Gene/L

Solano-Quinde, Lizandro; Bode, Brett

doi:10.1109/eit.2008.4554349

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…Event filtering is described in [18], and fault/failure prediction and analysis in [17] and [19]. [29] carries through to simulation based experimental evaluation of predictions. [8] builds very thorough models of reliability for BlueGene systems and also give a very careful review of recent work on reliability models and analyses.…”

Section: Related Workmentioning

confidence: 99%

End-to-end framework for fault management for open source clusters

Hammond

Minyard

Browne

2010

Proceedings of the 2010 TeraGrid Conference

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The scale and complexity of both hardware and software on large open source software systems such as Ranger make occurrence of faults and failures inevitable. What is not inevitable is that they should be allowed to go undetected, nor that diagnosis and recovery from failures should continue to be largely manual and effort intensive. This paper presents a framework for end-to-end fault management for open source clusters which is being developed on Ranger, but which targets general open source software based clusters. The elements of the framework are: a rationalized system logging stack for Linux, low overhead log and status monitoring, and a multilevel suite of diagnostic analyses. This paper describes this framework, presents the accomplishments to date, the results which have been obtained with the elements of the framework which are in place, and the plans for future development including a solicitation for collaboration on the project.

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Section: Related Workmentioning

confidence: 99%

End-to-end framework for fault management for open source clusters

Hammond

Minyard

Browne

2010

Proceedings of the 2010 TeraGrid Conference

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“…However, there are several works in Network Fault Prediction among which [18], [36], [61]- [65] which treat the NFP problem without using ML methods and can show interesting results. One example is the case of Hood et al [61], where they show show that it may be possible to predict network failure several hops away in space and around 10 minutes away in time (further testing and specification needed).…”

Section: ML Methods For Network Fault Predictionmentioning

confidence: 99%

Fault Prediction for Heterogeneous Networks using Machine Learning : a Survey

Murphy¹,

Lepers²,

Lavignotte³

2022

Preprint

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<div>This manuscript provides a survey of Network Fault Prediction using Machine Learning from the years 1997 to 2021. Operational elements from the industry are shared to provide context. An overview of the different ML techniques that have been applied to Network Fault Prediction (NFP) is given. This is followed by a systematic review of the studies on NFP. Future research perspectives in the field are proposed.</div>

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“…For example, IBM cite a failure rate of 0.02 faults/month/TF on a BlueGene machine, which scales to around 1 fault/minute on an ExaFLOP system [21].…”

Section: Fault Tolerancementioning

confidence: 99%

Overview of the SpiNNaker System Architecture

Furber

Lester

Plana

et al. 2013

IEEE Trans. Comput.

556

401

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Abstract-SpiNNaker (a contraction of Spiking Neural Network Architecture) is a million-core computing engine whose flagship goal is to be able to simulate the behaviour of aggregates of up to a billion neurons in real time. It consists of an array of ARM9 cores, communicating via packets carried by a custom interconnect fabric. The packets are small (40 or 72 bits), and their transmission is brokered entirely by hardware, giving the overall engine an extremely high bisection bandwidth of over 5 billion packets/s. Three of the principle axioms of parallel machine design -memory coherence, synchronicity and determinismhave been discarded in the design without, surprisingly, compromising the ability to perform meaningful computations. A further attribute of the system is the acknowledgment, from the initial design stages, that the sheer size of the implementation will make component failures an inevitable aspect of day-to-day operation, and fault detection and recovery mechanisms have been built into the system at many levels of abstraction. This paper describes the architecture of the machine and outlines the underlying design philosophy; software and applications are to be described in detail elsewhere, and only introduced in passing here as necessary to illuminate the description.

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Module Prototype for Online Failure Prediction for the IBM Blue Gene/L

Cited by 7 publications

References 12 publications

End-to-end framework for fault management for open source clusters

End-to-end framework for fault management for open source clusters

Fault Prediction for Heterogeneous Networks using Machine Learning : a Survey

Overview of the SpiNNaker System Architecture

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