Automatic reduction of execution trace data volume using gradient boosting in large-scale microservice systems

Amir Haghshenas, Nasser Ezzati-Jivan and Michel Dagenais

Paper (2024)

Open Acess document at official publisher

An external link is available for this item

Department:	Department of Computer Engineering and Software Engineering
PolyPublie URL:	https://publications.polymtl.ca/58644/
Conference Title:	37th Canadian Conference on Artificial Intelligence (Canadian AI 2024)
Conference Location:	Guelph, ON, Canada
Conference Date(s):	2024-05-27 - 2024-05-31
Official URL:	https://caiac.pubpub.org/pub/dh3zxquj
Date Deposited:	26 Jun 2024 12:51
Last Modified:	25 Sep 2024 16:51

Cite in APA 7:	Haghshenas, A., Ezzati-Jivan, N., & Dagenais, M. (2024, May). Automatic reduction of execution trace data volume using gradient boosting in large-scale microservice systems [Paper]. 37th Canadian Conference on Artificial Intelligence (Canadian AI 2024), Guelph, ON, Canada (12 pages). https://caiac.pubpub.org/pub/dh3zxquj
APA 7 (HTML)ASCII CitationAtomBibTeXDOIsDublin CoreEndNote (.ENW)MARC (ISO 2709/UTF-8) (.MRC)MARC (UTF-8) (.MRK8)MARC XMLMPEG-21 DIDLMultiline CSVOAI-ORE Resource Map (Atom Format)OPENAIREOpenURL ContextObjectOpenURL ContextObject in SpanRDF+N-TriplesRDF+N3RDF+XMLReferReference Manager (.RIS)Simple Metadata

Statistics

Repository Staff Only