research-article

Can we Predict Performance Events with Time Series Data from Monitoring Multiple Systems?

Authors:

Andreas Schörgenhumer,

Mario Kahlhofer,

Paul Grünbacher,

Hanspeter MössenböckAuthors Info & Claims

ICPE '19: Companion of the 2019 ACM/SPEC International Conference on Performance Engineering

Pages 9 - 12

https://doi.org/10.1145/3302541.3313101

Published: 27 March 2019 Publication History

Abstract

Predicting performance-related events is an important part of proactive fault management. As a result, many approaches exist for the context of single systems. Surprisingly, despite its potential benefits, multi-system event prediction, i.e., using data from multiple, independent systems, has received less attention. We present ongoing work towards an approach for multi-system event prediction that works with limited data and can predict events for new systems. We present initial results showing the feasibility of our approach. Our preliminary evaluation is based on 20 days of continuous, preprocessed monitoring time series data of 90 independent systems. We created five multi-system machine learning models and compared them to the performance of single-system machine learning models. The results show promising prediction capabilities with accuracies and F1-scores over 90% and false-positive-rates below 10%.

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Cited By

Luchtenberg RFernandes ALiebel GParreira WDazzi RFurbringer A(2023)APRENDIZADO DE MÁQUINA EM AMBIENTES HOSPITALARES: UM ESTUDO DE ANÁLISE DE TENDÊNCIAS DE SOBRECARGA EM SISTEMAS DE TECNOLOGIAS DA INFORMAÇÃO E COMUNICAÇÃORevista Contemporânea10.56083/RCV3N9-1273:9(15866-15893)Online publication date: 27-Sep-2023
https://doi.org/10.56083/RCV3N9-127
Weninger MGander EMössenböck H(2021)Guided ExplorationProceedings of the ACM on Human-Computer Interaction10.1145/34617315:EICS(1-34)Online publication date: 29-May-2021
https://dl.acm.org/doi/10.1145/3461731
Grohmann JHerbst NChalbani AArian YPeretz NKounev S(2020)A Taxonomy of Techniques for SLO Failure Prediction in Software SystemsComputers10.3390/computers90100109:1(10)Online publication date: 11-Feb-2020
https://doi.org/10.3390/computers9010010
Show More Cited By

Index Terms

Can we Predict Performance Events with Time Series Data from Monitoring Multiple Systems?
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Operational analysis
  2. Software organization and properties
    1. Extra-functional properties
      1. Software performance

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Published In

cover image ACM Conferences

ICPE '19: Companion of the 2019 ACM/SPEC International Conference on Performance Engineering

March 2019

99 pages

ISBN:9781450362863

DOI:10.1145/3302541

General Chairs:
Varsha Apte
IIT Bombay, India
,
Antinisca Di Marco
University of L'Aquila, Italy
,
Program Chairs:
Marin Litoiu
York University, Canada
,
José Merseguer
Universidad de Zaragoza, Spain

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 March 2019

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Christian Doppler Forschungsgesellschaft

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ICPE '19

Sponsor:

ICPE '19: Tenth ACM/SPEC International Conference on Performance Engineering

April 7 - 11, 2019

Mumbai, India

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Overall Acceptance Rate 252 of 851 submissions, 30%

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Cited By

Luchtenberg RFernandes ALiebel GParreira WDazzi RFurbringer A(2023)APRENDIZADO DE MÁQUINA EM AMBIENTES HOSPITALARES: UM ESTUDO DE ANÁLISE DE TENDÊNCIAS DE SOBRECARGA EM SISTEMAS DE TECNOLOGIAS DA INFORMAÇÃO E COMUNICAÇÃORevista Contemporânea10.56083/RCV3N9-1273:9(15866-15893)Online publication date: 27-Sep-2023
https://doi.org/10.56083/RCV3N9-127
Weninger MGander EMössenböck H(2021)Guided ExplorationProceedings of the ACM on Human-Computer Interaction10.1145/34617315:EICS(1-34)Online publication date: 29-May-2021
https://dl.acm.org/doi/10.1145/3461731
Grohmann JHerbst NChalbani AArian YPeretz NKounev S(2020)A Taxonomy of Techniques for SLO Failure Prediction in Software SystemsComputers10.3390/computers90100109:1(10)Online publication date: 11-Feb-2020
https://doi.org/10.3390/computers9010010
Das DSchiewe MBrighton EFuller MCerny TBures MFrajtak KShin DTisnovsky P(2020)Failure Prediction by Utilizing Log AnalysisProceedings of the International Conference on Research in Adaptive and Convergent Systems10.1145/3400286.3418263(188-195)Online publication date: 13-Oct-2020
https://dl.acm.org/doi/10.1145/3400286.3418263

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