Pinpoint: problem determination in large, dynamic Internet services

By Mike Y. Chen, Emre Kıcıman, Eugene Fratkin, Armando Fox, Eric A. Brewer. Proceedings : International Conference on Dependable Systems and Networks, 2002

949 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/7b9875c1c4ce06651b2801b7dd0408466f691e12

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Citations in the corpus

Microreboot - A Technique for Cheap Recovery (2004, background): “ of reported user-visible failures. By enabling sloppier fault detection, we make a number of detection and diagnosis solutions more useful. For example, statistical learning approaches like Pinpoint [9], while prone to false positives, are useful for systems whose structure is not known. CombiningP”
Recovery Oriented Computing (ROC): Motivation, Definition, Techniques, and Case Studies (2002, positive): “[Jain 1988] For more detail, see [Chen02].”
Carat: collaborative energy diagnosis for mobile devices (2013, background): “Pinpoint [6] and Magpie [4] track communication dependencies with the aim of isolating the root cause of misbehavior; they require instrumentation of the application to tag client requests.”
On the Accuracy of Spectrum-based Fault Localization (2007, methodology): “In earlier work [1], we obtained preliminary evidence that the Ochiai similarity coefficient, known from the biology domain, can improve diagnostic accuracy over eight other coefficients, including those used by the Pinpoint and Tarantula tools mentioned above.”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “[92] employ clustering to group the successes and failures of requests.”
A practical evaluation of spectrum-based fault localization (2009, methodology): “Examples of tools that implement this approach are Pinpoint (Chen et al., 2002), which focuses on large, dynamic on-line transaction processing systems (Jones et al., 2002) whose implementation focuses on the analysis of C programs, and AMPLE (Dallmeier et al., 2005), which focuses on…”
A theoretical analysis of the risk evaluation formulas for spectrum-based fault localization (2013, methodology): “Many formulas have been proposed for SBFL, which included Tarantula [Jones et al. 2002], Jaccard [Chen et al. 2002], Ochiai [Abreu et al. 2006], three formulas proposed by Wong et al. [2007] (which are referred to as Wong1, Wong2 and Wong3, respectively, in this article), etc. [Reps et al. 1997;…”
Detecting large-scale system problems by mining console logs (2009, background): “, 2002; Ma & Hellerstein, 2001) to more detailed tracing such as execution paths (Chen et al., 2002; Chen & Brewer, 2004).”
Failure diagnosis using decision trees (2004, background): “Our earlier work, Pinpoint [6] and ObsLogs at Tellme Networks [5], also dynamically trace request paths through tiered systems.”
Angelix: Scalable Multiline Program Patch Synthesis via Symbolic Analysis (2016, methodology): “We use the Jaccard formula [7], considered most eﬀective for automated program repair according to [32].”
X-Trace: A Pervasive Network Tracing Framework (2007, methodology): “Instrumenting concurrency libraries and runtime environments may ease or automate this propagation [9, 20, 8].”
An Evaluation of Similarity Coefficients for Software Fault Localization (2006, methodology): “, [11]), is used in the Pinpoint framework [3].”
Dapper, a Large-Scale Distributed Systems Tracing Infrastructure (2010, background): “Annotation-based schemes [3, 12, 9, 16] rely on applications or middleware to explicitly tag every record with a global identiﬁer that links these message records back to the originating request.”
FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices (2020, methodology): “Pinpoint [16] runs clustering analysis on Jaccard similarity coefficient to determine the components that are mostly correlated with the failure.”