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Virtualization technology has many attractive qualities including improved security, reliability, scalability, and resource sharing/management. As a result, virtualization has been deployed on an array of platforms, from mobile devices to high end enterprise servers. In this paper, we present a novel approach to working at a virtualization interface, performing workload characterization equipped with the information available at the virtual machine monitor (VMM) interface. Due to the semantic gap between the raw VMM-level data available and the true application behavior, we employ the power of regression techniques to extract meaningful information about a workload's behavior. We also demonstrate that the information available at the VMM level still retains rich workload characteristics that can be used to identify application behavior. We show that we are able to capture enough information about a workload to characterize and decompose it into a combination of CPU, memory, disk I/O, and network I/O-intensive components. Dissecting the behavior of a workload in terms of these components, we can develop significant insight into the behavior of any application. Workload characterization can be used for online performance monitoring, workload scheduling, workload trending, virtual machine (VM)health monitoring, and security analysis. We can also consider how VMM-based workload profiles can be used to detect anomalous behavior in virtualized environments by comparing a model of potentially malicious execution to that of normal execution.
Azmandian et al. (Fri,) studied this question.