Visual Analysis of Cloud Computing Performance Using Behavioral Lines

Chris Muelder, Biao Zhu, Wei Chen, Hongxin Zhang, Kwan-Liu Ma

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

Cloud computing is an essential technology to Big Data analytics and services. A cloud computing system is often comprised of a large number of parallel computing and storage devices. Monitoring the usage and performance of such a system is important for efficient operations, maintenance, and security. Tracing every application on a large cloud system is untenable due to scale and privacy issues. But profile data can be collected relatively efficiently by regularly sampling the state of the system, including properties such as CPU load, memory usage, network usage, and others, creating a set of multivariate time series for each system. Adequate tools for studying such large-scale, multidimensional data are lacking. In this paper, we present a visual based analysis approach to understanding and analyzing the performance and behavior of cloud computing systems. Our design is based on similarity measures and a layout method to portray the behavior of each compute node over time. When visualizing a large number of behavioral lines together, distinct patterns often appear suggesting particular types of performance bottleneck. The resulting system provides multiple linked views, which allow the user to interactively explore the data by examining the data or a selected subset at different levels of detail. Our case studies, which use datasets collected from two different cloud systems, show that this visual based approach is effective in identifying trends and anomalies of the systems.

Original languageEnglish (US)
Article number7422127
Pages (from-to)1694-1704
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Volume22
Issue number6
DOIs
StatePublished - Jun 1 2016

Keywords

  • Cloud computing
  • multidimensional data
  • performance visualization
  • visual analytics

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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