Cognitive visual analytics of multi-dimensional cloud system monitoring data

Abstract

Hardware virtualization has enabled large scale computational service delivery models with significant cost leverage and has improved resource utilization of cloud computing platforms. This has completely changed the landscape of computing in the last decade. It has enabled very large-scale data analytics through distributed, high performance computing. However, due to the infrastructure complexity, end-users and administrators of cloud platforms can rarely obtain a complete 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 often fall short of maximizing the 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. New operating platforms for cloud management and service provisioning allow live migration and dynamic resource re-allocation at multiple levels of the hardware virtualization layers. Hence, it has become necessary to provide cognitive visualizing tools for monitoring the activities in an active cloud environment. In this work, we describe a perceptual-based interactive visualization platform that gives users and administrators a cognitive view of cloud computing system dynamics. We define machine states and aggregate states at multiple levels of detail to construct a multiview presentation of the resource utilization according to the scalability and the elasticity features of a cloud computing system.