Fri 19 Jul 2024 11:54 - 12:12 at Pitomba - AI4SE 4 Chair(s): Wesley Assunção

Predicting failures in production environments allows service providers to activate countermeasures that prevent harming the users of the applications. The most successful approaches predict failures from error states that the current approaches identify from anomalies in time series of fixed sets of KPI values collected at runtime. They cannot handle time series of KPI sets with size that varies over time. Thus these approaches work with applications that run on statically configured sets of components and computational nodes, and do not scale up to the many popular cloud applications that exploit autoscaling.

This paper proposes PREFACE, a novel approach to predict failures in cloud applications that exploit autoscaling. PREFACE originally augments the neural-network-based failure predictors successfully exploited to predict failures in statically configured applications, with a Rectifier layer that handles KPI sets of highly variable size as the ones collected in cloud autoscaling applications, and reduces those KPIs to a set of rectified-KPIs of fixed size that can be fed to the neural-network predictor. The PREFACE Rectifier computes the rectified-KPIs as descriptive statistics of the original KPIs, for each logical component of the target application. The descriptive statistics shrink the highly variable sets of KPIs collected at different timestamps to a fixed set of values compatible with the input nodes of the neural-network failure predictor. The neural network can then reveal anomalies that correspond to error states, before they propagate to failures that harm the users of the applications. The experiments on both a commercial application and a widely used academic exemplar confirm that PREFACE can indeed predict many harmful failures early enough to activate proper countermeasures.

Fri 19 Jul

Displayed time zone: Brasilia, Distrito Federal, Brazil change

11:00 - 12:30
AI4SE 4 Research Papers at Pitomba
Chair(s): Wesley Assunção North Carolina State University
11:00
18m
Talk
Improving the Learning of Code Review Successive Tasks with Cross-Task Knowledge DistillationDistinguished Paper Award
Research Papers
Oussama Ben Sghaier DIRO, Université de Montréal, Houari Sahraoui DIRO, Université de Montréal
11:18
18m
Talk
Learning to Detect and Localize Multilingual Bugs
Research Papers
Haoran Yang Washington State University, Yu Nong Washington State University, Tao Zhang Macau University of Science and Technology, Xiapu Luo The Hong Kong Polytechnic University, Haipeng Cai Washington State University
DOI Pre-print
11:36
18m
Talk
Mining Action Rules for Defect Reduction Planning
Research Papers
Khouloud Oueslati Polytechnique Montréal, Canada, Gabriel Laberge Polytechnique Montréal, Canada, Maxime Lamothe Polytechnique Montreal, Foutse Khomh Polytechnique Montréal
11:54
18m
Talk
Predicting Failures of Autoscaling Distributed Applications
Research Papers
Giovanni Denaro University of Milano - Bicocca, Noura El Moussa USI Università della Svizzera Italiana & SIT Schaffhausen Institute of Technology, Rahim Heydarov USI Università della Svizzera Italiana, Francesco Lomio SIT Schaffhausen Institute of Technology, Mauro Pezze USI Università della Svizzera Italiana & SIT Schaffhausen Institute of Technology, Ketai Qiu USI Università della Svizzera Italiana
DOI Pre-print
12:12
18m
Talk
RavenBuild: Context, Relevance, and Dependency Aware Build Outcome Prediction
Research Papers
Gengyi Sun University of Waterloo, Sarra Habchi Ubisoft Montréal, Shane McIntosh University of Waterloo