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ASE 2021
Mon 15 - Fri 19 November 2021 Australia

This program is tentative and subject to change.

Wed 17 Nov 2021 22:20 - 22:40 at Koala - Performance

Service reliability is one of the key challenges that cloud providers have to deal with. In cloud systems, unplanned service failures may cause severe cascading impacts on their dependent services, deteriorating customer satisfaction. Predicting the cascading impacts accurately and efficiently is critical to the operation and maintenance of cloud systems. Existing approaches identify whether one service depends on another via distributed tracing but no prior work focused on discriminating the intensity of the dependency between cloud services. In this paper, we empirically study the outages and the procedure for failure diagnosis in two cloud providers to motivate the definition of the intensity of dependency. Then we propose AIM, the first approach to predict the intensity of dependencies between cloud microservices. AIM first generates a set of candidate dependency pairs from the spans. AIM then represents the status of each cloud service with a multivariate time series aggregated from the spans. With the representation of services, AIM calculates the similarities between the statuses of the caller and callee of each candidate pair. Finally, AIM aggregates the similarities to produce a unified value as the intensity of the dependency. We evaluate AIM on the data collected from an open-source microservice benchmark and a cloud system in production. The experimental results show that AIM can efficiently and accurately predict the intensity of dependencies. We further demonstrate the usefulness of our method in a large-scale cloud system. We plan to release both datasets to facilitate future studies.

This program is tentative and subject to change.

Wed 17 Nov

Displayed time zone: Hobart change

22:00 - 23:00
"What makes my queries slow?": Subgroup Discovery for SQL Workload Analysis
Research Papers
Youcef Remil Infologic, INSA Lyon, Anes Bendimerad Infologic, Romain Mathonat Infologic, Philippe Chaleat Infologic, Mehdi Kaytoue INFOLOGIC
AID: Efficient Prediction of Aggregated Intensity of Dependency in Large-scale Cloud Systems
Research Papers
Tianyi Yang The Chinese University of Hong Kong, Jiacheng Shen The Chinese University of Hong Kong, Yuxin Su The Chinese University of Hong Kong, Xiao Ling Huawei Technologies, Yongqiang Yang Huawei Technologies, Michael Lyu The Chinese University of Hong Kong
Assessment of Off-the-Shelf SE-specific Sentiment Analysis Tools: An Extended Replication Study
Journal-first Papers
Nicole Novielli University of Bari, Fabio Calefato University of Bari, Filippo Lanubile University of Bari, Alexander Serebrenik Eindhoven University of Technology
EvoMe: A Software Evolution Management Engine Based on Differential Factbase
Tool Demonstrations
Xiuheng Wu Nanyang Technological University, Mengyang Li Nanyang Technological University, Yi Li Nanyang Technological University, Singapore