Enhanced execution trace abstraction approach using social network analysis methods
| dc.contributor.author | Wang, Ji | |
| dc.contributor.author | Ezzati-Jivan, Naser | |
| dc.contributor.editor | Kelter, Udo | |
| dc.date.accessioned | 2022-11-24T10:42:05Z | |
| dc.date.available | 2022-11-24T10:42:05Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | In this paper, we propose an improvement in system execution tracing by applying social network analysis techniques on the trace data. We perform a 3-step analysis: collection of trace data on operating system kernel; community analysis on the data; and PageRank algorithm within each community. The proposed analysis focused on the following problems: useless information contained in the data and the enormous size of the data. We propose two use cases: one on kernel trace filtering and the other on virtual machine clustering. Our evaluation shows that the proposed method provided a concise and more comprehensive view of the trace data. This can help shorten the time and assist in building infrastructural functions in analyzing system execution. | en |
| dc.identifier.pissn | 0720-8928 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/39792 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | Softwaretechnik-Trends Band 40, Heft 3 | |
| dc.relation.ispartofseries | Softwaretechnik-Trends | |
| dc.subject | social network analysis | |
| dc.subject | tracing | |
| dc.subject | trace data virtual machine clustering | |
| dc.title | Enhanced execution trace abstraction approach using social network analysis methods | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 60 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 58 | |
| gi.conference.date | 44147 | |
| gi.conference.location | Leipzig | |
| gi.conference.sessiontitle | Symposium on Software Performance (SSP) |
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