ACSOS 2021
Mon 27 September - Fri 1 October 2021 Washington, DC, United States

Cloud services are important for healthcare, banking, communication, and other purposes. Inevitably, such services fail, harming the processes and disturbing the people that depend on them. With the rapid increase in the use of cloud services, especially in 2020 during the COVID-19 period, more failures are expected to occur in cloud services. Understanding failure in cloud services is challenging, but important to help preventing them.

Much work has studied failure logs and reports provided by infrastructure operators. However, there is a paucity of information about how users perceive the failures of cloud services. In this work, we collect user-reported failures and characterize them empirically. We collect failures reported by users to the trusted aggregator Outage Report for 12 cloud services over 16 months spread across 2019 and 2020. We show evidence that user-reported failures not only capture major failures also self-reported by cloud operators, but also provide information about additional failures. We count and analyze time patterns in these reports. We further derive failures from sets of reports and characterize their duration and interarrival time. We make 9~main observations about how users perceive failure in cloud services. We find over 10x differences in request failure rates across microservice structures when using user reported traces compared to using constant a failure distribution. Overall, our study provides the first long-term characterization of user-reported cloud failures.

Wed 29 Sep

Displayed time zone: Eastern Time (US & Canada) change

11:45 - 12:50
Resource Management in Data Centers and Cloud Computing IMain Track at AUDITORIUM 1
Chair(s): Vana Kalogeraki Athens University of Economics and Business, Samuel Kounev University of W├╝rzburg, Germany
FaaSRank: Learning to Schedule Functions in Serverless Platforms
Main Track
Hanfei Yu University of Washington, Tacoma, Athirai Irissappane University of Washington, Tacoma, Hao Wang Louisiana State University, USA, Wes Loyd University of Washington, Tacoma
Many Models at the Edge: Characterizing and Improving Deep Inference via Model-Level Caching
Main Track
Samuel Odgen Worcester Polytechnic Institute, Guin Gilman Worcester Polytechnic Institute, Robert Walls Worcester Polytechnic Institute, Tian Guo Worcester Polytechnic Institute
Empirical Characterization of User Reports About Cloud FailuresIEEE ROR-R
Main Track
Sacheendra Talluri Vrije Universiteit Amsterdam, Netherlands, Leon Overweel Dexter Energy, Laurens Versluis Vrije Universiteit Amsterdam, Animesh Trivedi Vrije Universiteit Amsterdam, Alexandru Iosup Vrije Universiteit Amsterdam