Pivot Tracing: Dynamic Causal Monitoring for Distributed Systems.
Monitoring and troubleshooting distributed systems are notoriously difficult; potential problems are complex, varied, and unpredictable. The monitoring and diagnosis tools commonly used today—logs, counters, and metrics—have two important limitations: what gets recorded is defined a priori, and the...
| Publicado en: | Communications of the ACM Vol. 63; no. 3; pp. 94 - 103 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Association for Computing Machinery
Mar2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=141926857&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 141926857 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Mar2020 vid: 63 iid: 3 pid: 68 pub: Association for Computing Machinery artinfo: ui: 141926857 10.1145/3378933 ppf: 94 ppct: 9 formats: tig: atl: Pivot Tracing: Dynamic Causal Monitoring for Distributed Systems. aug: au: Mace, Jonathan Roelke, Ryan Fonseca, Rodrigo affil: Brown University Department of Computer Science, Providence, RI, USA. su: Computer systems Debugging Computer science Electronic data processing Query languages (Computer science) sug: subj: Computer systems Debugging Computer science Electronic data processing Query languages (Computer science) ab: Monitoring and troubleshooting distributed systems are notoriously difficult; potential problems are complex, varied, and unpredictable. The monitoring and diagnosis tools commonly used today—logs, counters, and metrics—have two important limitations: what gets recorded is defined a priori, and the information is recorded in a component- or machine-centric way, making it extremely hard to correlate events that cross these boundaries. This paper presents Pivot Tracing, a monitoring framework for distributed systems that addresses both limitations by combining dynamic instrumentation with a novel relational operator: the happened-before join. Pivot Tracing gives users, at runtime, the ability to define arbitrary metrics at one point of the system, while being able to select, filter, and group by events meaningful at other parts of the system, even when crossing component or machine boundaries. Pivot Tracing does not correlate cross-component events using expensive global aggregations, nor does it perform offline analysis. Instead, Pivot Tracing directly correlates events as they happen by piggybacking metadata alongside requests as they execute. This gives Pivot Tracing low runtime overhead—less than 1% for many cross-component monitoring queries. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2020 holdings: @attributes: islocal: N |
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