Dissertation/ Thesis
Utilizing Runtime Information for Accurate Root Cause Identification in Performance Diagnosis
| Τίτλος: | Utilizing Runtime Information for Accurate Root Cause Identification in Performance Diagnosis |
|---|---|
| Συγγραφείς: | Weng, Lingmei |
| Έτος έκδοσης: | 2023 |
| Συλλογή: | Columbia University: Academic Commons |
| Θεματικοί όροι: | Computer science, Debugging in computer science--Computer programs, Computer software--Evaluation |
| Περιγραφή: | This dissertation highlights that existing performance diagnostic tools often become less effective due to their inherent inaccuracies in modern software. To overcome these inaccuracies and effectively identify the root causes of performance issues, it is necessary to incorporate supplementary runtime information into these tools. Within this context, the dissertation integrates specific runtime information into two typical performance diagnostic tools: profilers and causal tracing tools. The integration yields a substantial enhancement in the effectiveness of performance diagnosis. Among these tools, gprof stands out as a representative profiler for performance diagnosis. Nonetheless, its effectiveness diminishes as the time cost calculated based on CPU sampling fails to accurately and adequately pinpoint the root causes of performance issues in complex software. To tackle this challenge, the dissertation introduces an innovative methodology called value-assisted cost profiling (vProf). This approach incorporates variable values observed during runtime into the profiling process. By continuously sampling variable values from both normal and problematic executions, vProf refines function cost estimates, identifies anomalies in value distributions, and highlights potentially problematic code areas that could be the actual sources of performance is- sues. The effectiveness of vProf is validated through the diagnosis of 18 real-world performance is- sues in four widely-used applications. Remarkably, vProf outperforms other state-of-the-art tools, successfully diagnosing all issues, including three that had remained unresolved for over four years. Causal tracing tools reveal the root causes of performance issues in complex software by generating tracing graphs. However, these graphs often suffer from inherent inaccuracies, characterized by superfluous (over-connected) and missed (under-connected) edges. These inaccuracies arise from the diversity of programming paradigms. To mitigate the inaccuracies, the ... |
| Τύπος εγγράφου: | thesis |
| Γλώσσα: | English |
| DOI: | 10.7916/mhn8-vv84 |
| Διαθεσιμότητα: | https://doi.org/10.7916/mhn8-vv84 |
| Αριθμός Καταχώρησης: | edsbas.EC303428 |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://doi.org/10.7916/mhn8-vv84# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: Utilizing Runtime Information for Accurate Root Cause Identification in Performance Diagnosis – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Weng%2C+Lingmei%22">Weng, Lingmei</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2023 – Name: Subset Label: Collection Group: HoldingsInfo Data: Columbia University: Academic Commons – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+science%22">Computer science</searchLink><br /><searchLink fieldCode="DE" term="%22Debugging+in+computer+science--Computer+programs%22">Debugging in computer science--Computer programs</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software--Evaluation%22">Computer software--Evaluation</searchLink> – Name: Abstract Label: Description Group: Ab Data: This dissertation highlights that existing performance diagnostic tools often become less effective due to their inherent inaccuracies in modern software. To overcome these inaccuracies and effectively identify the root causes of performance issues, it is necessary to incorporate supplementary runtime information into these tools. Within this context, the dissertation integrates specific runtime information into two typical performance diagnostic tools: profilers and causal tracing tools. The integration yields a substantial enhancement in the effectiveness of performance diagnosis. Among these tools, gprof stands out as a representative profiler for performance diagnosis. Nonetheless, its effectiveness diminishes as the time cost calculated based on CPU sampling fails to accurately and adequately pinpoint the root causes of performance issues in complex software. To tackle this challenge, the dissertation introduces an innovative methodology called value-assisted cost profiling (vProf). This approach incorporates variable values observed during runtime into the profiling process. By continuously sampling variable values from both normal and problematic executions, vProf refines function cost estimates, identifies anomalies in value distributions, and highlights potentially problematic code areas that could be the actual sources of performance is- sues. The effectiveness of vProf is validated through the diagnosis of 18 real-world performance is- sues in four widely-used applications. Remarkably, vProf outperforms other state-of-the-art tools, successfully diagnosing all issues, including three that had remained unresolved for over four years. Causal tracing tools reveal the root causes of performance issues in complex software by generating tracing graphs. However, these graphs often suffer from inherent inaccuracies, characterized by superfluous (over-connected) and missed (under-connected) edges. These inaccuracies arise from the diversity of programming paradigms. To mitigate the inaccuracies, the ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: thesis – Name: Language Label: Language Group: Lang Data: English – Name: DOI Label: DOI Group: ID Data: 10.7916/mhn8-vv84 – Name: URL Label: Availability Group: URL Data: https://doi.org/10.7916/mhn8-vv84 – Name: AN Label: Accession Number Group: ID Data: edsbas.EC303428 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EC303428 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.7916/mhn8-vv84 Languages: – Text: English Subjects: – SubjectFull: Computer science Type: general – SubjectFull: Debugging in computer science--Computer programs Type: general – SubjectFull: Computer software--Evaluation Type: general Titles: – TitleFull: Utilizing Runtime Information for Accurate Root Cause Identification in Performance Diagnosis Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Weng, Lingmei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2023 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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