Next Article in Journal
Modeling Bimodal Social Networks Subject to the Recommendation with the Cold Start User-Item Model
Next Article in Special Issue
Self-Adaptive Data Processing to Improve SLOs for Dynamic IoT Workloads
Previous Article in Journal
Leveraging Blockchain Technology to Break the Cloud Computing Market Monopoly
Previous Article in Special Issue
Towards Self-Aware Multirotor Formations
Open AccessArticle

A Taxonomy of Techniques for SLO Failure Prediction in Software Systems

1
Chair of Software Engineering, University of Würzburg, Am Hubland, 97074 Würzburg, Germany
2
Tel-Aviv Yafo research center for Huawei Technologies, 45101 Hod Hasharon, Israel
*
Author to whom correspondence should be addressed.
Computers 2020, 9(1), 10; https://doi.org/10.3390/computers9010010
Received: 31 December 2019 / Revised: 4 February 2020 / Accepted: 5 February 2020 / Published: 11 February 2020
(This article belongs to the Special Issue Applications in Self-Aware Computing Systems and their Evaluation)
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.
Keywords: taxonomy; survey; failure prediction; anomaly prediction; anomaly detection; self-aware computing; self-adaptive systems; performance prediction taxonomy; survey; failure prediction; anomaly prediction; anomaly detection; self-aware computing; self-adaptive systems; performance prediction
MDPI and ACS Style

Grohmann, J.; Herbst, N.; Chalbani, A.; Arian, Y.; Peretz, N.; Kounev, S. A Taxonomy of Techniques for SLO Failure Prediction in Software Systems. Computers 2020, 9, 10.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop