Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

3-2022

Abstract

Cloud systems are becoming increasingly powerful and complex. It is highly challenging to identify anomalous execution behaviors and pinpoint problems by examining the overwhelming intermediate results/states in complex application workflows. Domain scientists urgently need a friendly and functional interface to understand the quality of the computing services and the performance of their applications in real time. To meet these needs, we explore data generated by job schedulers and investigate general performance metrics (e.g., utilization of CPU, memory and disk I/O). Specifically, we propose an interactive visual analytics approach, BatchLens, to provide both providers and users of cloud service with an intuitive and effective way to explore the status of system batch jobs and help them conduct root-cause analysis of anomalous behaviors in batch jobs. We demonstrate the effectiveness of BatchLens through a case study on the public Alibaba bench workload trace datasets.

Keywords

Cloud computing, Human-computer interaction, Visual analytics

Discipline

Computer Engineering | Data Storage Systems

Research Areas

Information Systems and Management

Publication

2022 Design, Automation & Test in Europe (DATE): Antwerp, Belgium, March 14-23: Proceedings

First Page

108

Last Page

111

ISBN

9781665496377

Identifier

10.23919/DATE54114.2022.9774668

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.23919/DATE54114.2022.9774668

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