A Taxonomy of Techniques for SLO Failure Prediction in Software Systems

Computers

Samuel Kounev¹  Johannes Grohmann¹  Nikolas Herbst¹  Noam Peretz²  Yair Arian²  Avi Chalbani² 

[1] Chair of Software Engineering, University of Würzburg, Am Hubland, 97074 Würzburg, Germany;Tel-Aviv Yafo research center for Huawei Technologies, 45101 Hod Hasharon, Israel;

关键词: taxonomy;    survey;    failure prediction;    anomaly prediction;    anomaly detection;    self-aware computing;    self-adaptive systems;    performance prediction;   

DOI  :  10.3390/computers9010010

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown