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Data-Driven Techniques in Computing System Management

Authors:

Yue HuangAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 50, Issue 3

Article No.: 45, Pages 1 - 43

https://doi.org/10.1145/3092697

Published: 27 July 2017 Publication History

Abstract

Modern forms of computing systems are becoming progressively more complex, with an increasing number of heterogeneous hardware and software components. As a result, it is quite challenging to manage these complex systems and meet the requirements in manageability, dependability, and performance that are demanded by enterprise customers. This survey presents a variety of data-driven techniques and applications with a focus on computing system management. In particular, the survey introduces intelligent methods for event generation that can transform diverse log data sources into structured events, reviews different types of event patterns and the corresponding event-mining techniques, and summarizes various event summarization methods and data-driven approaches for problem diagnosis in system management. We hope this survey will provide a good overview for data-driven techniques in computing system management.

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Yu QZhao NLi MLi ZWang HZhang WSui KPei D(2024)A survey on intelligent management of alerts and incidents in IT servicesJournal of Network and Computer Applications10.1016/j.jnca.2024.103842224(103842)Online publication date: Apr-2024
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Ramos Gutiérrez BReina Quintero AParody LGómez López M(2023)When business processes meet complex events in logisticsComputers in Industry10.1016/j.compind.2022.103788144:COnline publication date: 1-Jan-2023
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cover image ACM Computing Surveys

ACM Computing Surveys Volume 50, Issue 3

May 2018

550 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3101309

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

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Copyright © 2017 ACM.

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Association for Computing Machinery

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Publication History

Published: 27 July 2017

Accepted: 01 April 2017

Revised: 01 December 2016

Received: 01 June 2016

Published in CSUR Volume 50, Issue 3

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Ministry of Education/China Mobile joint research
Scientific and Technological Support Project (Society) of Jiangsu
FIU Dissertation Year Fellowship
Chinese National Natural Science Foundation
National Science Foundation

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Yu QZhao NLi MLi ZWang HZhang WSui KPei D(2024)A survey on intelligent management of alerts and incidents in IT servicesJournal of Network and Computer Applications10.1016/j.jnca.2024.103842224(103842)Online publication date: Apr-2024
https://doi.org/10.1016/j.jnca.2024.103842
Ramos Gutiérrez BReina Quintero AParody LGómez López M(2023)When business processes meet complex events in logisticsComputers in Industry10.1016/j.compind.2022.103788144:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.compind.2022.103788
Bose ATandon SBasu MGhosh S(2022)Social Media for Post-Disaster Relief: Mapping Needs and Availabilities to UNOCHA Resource ClassesProceedings of the 23rd International Conference on Distributed Computing and Networking10.1145/3491003.3493236(294-298)Online publication date: 4-Jan-2022
https://dl.acm.org/doi/10.1145/3491003.3493236
Cao LChen YZhang ZGui N(2021)A Multiattention-Based Supervised Feature Selection Method for Multivariate Time SeriesComputational Intelligence and Neuroscience10.1155/2021/69111922021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6911192
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Tian YSi LZhang XCheng RHe CTan KJin Y(2021)Evolutionary Large-Scale Multi-Objective Optimization: A SurveyACM Computing Surveys10.1145/347097154:8(1-34)Online publication date: 4-Oct-2021
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Abukmeil MFerrari SGenovese APiuri VScotti F(2021)A Survey of Unsupervised Generative Models for Exploratory Data Analysis and Representation LearningACM Computing Surveys10.1145/345096354:5(1-40)Online publication date: 9-Jul-2021
https://dl.acm.org/doi/10.1145/3450963
Zhao L(2021)Event Prediction in the Big Data EraACM Computing Surveys10.1145/345028754:5(1-37)Online publication date: 25-May-2021
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Yu KLiu LLi J(2021)A Unified View of Causal and Non-causal Feature SelectionACM Transactions on Knowledge Discovery from Data10.1145/343689115:4(1-46)Online publication date: 18-Apr-2021
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Gramuglia EStorvik GStakkeland M(2021)Clustering and Automatic Labelling Within Time Series of Categorical Observations—With an Application to Marine Log MessagesJournal of the Royal Statistical Society Series C: Applied Statistics10.1111/rssc.1248370:3(714-732)Online publication date: 4-Jun-2021
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