Cognitive Visual Analytics of Multi-Dimensional Cloud System Monitoring Data

Cognitive Visual Analytics of Multi-Dimensional Cloud System Monitoring Data

George Baciu (GAMA Lab, Department of Computing, Hong Kong Polytechnic University, Hung Hom, Hong Kong), Yungzhe Wang (Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong) and Chenhui Li (Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong)
DOI: 10.4018/IJSSCI.2017010102
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Abstract

Hardware virtualization has enabled large scale computational service delivery models with high cost leverage and improved resource utilization on cloud computing platforms. This has completely changed the landscape of computing in the last decade. It has also enabled large–scale data analytics through distributed high performance computing. Due to the infrastructure complexity, end–users and administrators of cloud platforms can rarely obtain a full picture of the state of cloud computing systems and data centers. Recent monitoring tools enable users to obtain large amounts of data with respect to many utilization parameters of cloud platforms. However, they fail to get the maximal overall insight into the resource utilization dynamics of cloud platforms. Furthermore, existing tools make it difficult to observe large-scale patterns, making it difficult to learn from the past behavior of cloud system dynamics. In this work, the authors describe a perceptual-based interactive visualization platform that gives users and administrators a cognitive view of cloud computing system dynamics.
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Contributions

In this work, we present a new design for interactive cognitive visualization to provide effective analytical capability of intelligent cloud systems. For each target, the data item composed of multiple parameters being monitored can be regarded as a point in a multi-dimensional space. The data items are collected over time at specific frequency commensurable with computing tasks, i.e. milliseconds.

Our contributions are as follows:

  • We put forward a framework to combine components in cloud monitoring and data visualization seamlessly. The visualization design aims at showing data patterns that users and administrators are mostly concerned about, for instance, underutilization or overutilization of resources, e.g. memory, disk, processors

  • By aggregation and clustering of raw monitoring data with new analytic approaches and reference vector settings, we aggregate monitoring targets into three states: cool, norm and warm. This is explained in section of Methods.

  • In order to adapt to the elastic features of a cloud system, we design a Level-of-Detail (LOD) visualization approach, and present its advantage with case studies.

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