A Taxonomy of Techniques for SLO Failure Prediction in Software Systems
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- Failure prediction is an important aspect of self-aware computing systems. Therefore, a multitude of different approaches has been proposed in the literature over the past few years. In this work, we propose a taxonomy for organizing works focusing on the prediction of Service Level Objective (SLO) failures. Our taxonomy classifies related work along the dimensions of the prediction target (e.g., anomaly detection, performance prediction, or failure prediction), the time horizon (e.g., detection or prediction, online or offline application),Failure prediction is an important aspect of self-aware computing systems. Therefore, a multitude of different approaches has been proposed in the literature over the past few years. In this work, we propose a taxonomy for organizing works focusing on the prediction of Service Level Objective (SLO) failures. Our taxonomy classifies related work along the dimensions of the prediction target (e.g., anomaly detection, performance prediction, or failure prediction), the time horizon (e.g., detection or prediction, online or offline application), and the applied modeling type (e.g., time series forecasting, machine learning, or queueing theory). The classification is derived based on a systematic mapping of relevant papers in the area. Additionally, we give an overview of different techniques in each sub-group and address remaining challenges in order to guide future research.…
Autor(en): | Johannes Grohmann, Nikolas Herbst, Avi Chalbani, Yair Arian, Noam Peretz, Samuel Kounev |
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URN: | urn:nbn:de:bvb:20-opus-200594 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Fakultät für Mathematik und Informatik / Institut für Informatik |
Sprache der Veröffentlichung: | Englisch |
Titel des übergeordneten Werkes / der Zeitschrift (Englisch): | Computers |
ISSN: | 2073-431X |
Erscheinungsjahr: | 2020 |
Band / Jahrgang: | 9 |
Heft / Ausgabe: | 1 |
Seitenangabe: | 10 |
Originalveröffentlichung / Quelle: | Computers 2020, 9(1), 10; https://doi.org/10.3390/computers9010010 |
DOI: | https://doi.org/10.3390/computers9010010 |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Freie Schlagwort(e): | anomaly detection; anomaly prediction; failure prediction; performance prediction; self-adaptive systems; self-aware computing; survey; taxonomy |
Datum der Freischaltung: | 31.08.2020 |
Datum der Erstveröffentlichung: | 11.02.2020 |
Lizenz (Deutsch): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |