BatchLens: A Visualization Approach for Analyzing Batch Jobs in Cloud Systems
Shaolun Ruan1,a, Yong Wang1,b, Hailong Jiang2,c, Weijia Xu3 and Qiang Guan2,d
1School of Computing and Information Systems Singapore Management University Singapore, Singapore
aslruan.2021@phdcs.smu.edu.sg
byongwang@smu.edu.sg
2Department of Computer Science Kent State University Kent, U.S
chjiang13@kent.edu
dqguan@kent.edu
3Scalable Computational Intelligence Group Texas Advanced Computing Center Austin, U.S
xwj@tacc.utexas.edu
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, Visual Analytics, Humancomputer Interaction.
