Host-Based Virtual Machine Workload Characterization Using Hypervisor Trace Mining

• Published in: ACM transactions on modeling and performance evaluation of computing systems 2021-06, Vol.6 (1), p.1-25, Article 4
• Main Authors: Nemati, Hani, Azhari, Seyed Vahid, Shakeri, Mahsa, Dagenais, Michel
• Format: Article
• Language: English
• Subjects: Clustering; Computing platforms; Data cleaning; Data mining; Designing software; Information systems; Information systems applications; Software and its engineering; Software creation and management; Software design techniques; Software implementation planning
• ISSN: 2376-3639; 2376-3647
• DOI: 10.1145/3460197

Cloud computing is a fast-growing technology that provides on-demand access to a pool of shared resources. This type of distributed and complex environment requires advanced resource management solutions that could model virtual machine (VM) behavior. Different workload measurements, such as CPU, memory, disk, and network usage, are usually derived from each VM to model resource utilization and group similar VMs. However, these course workload metrics require internal access to each VM with the available performance analysis toolkit, which is not feasible with many cloud environments privacy policies. In this article, we propose a non-intrusive host-based virtual machine workload characterization using hypervisor tracing. VM blockings duration, along with virtual interrupt injection rates, are derived as features to reveal multiple levels of resource intensiveness. In addition, the VM exit reason is considered, as well as the resource contention rate due to the host and other VMs. Moreover, the processes and threads preemption rates in each VM are extracted using the collected tracing logs. Our proposed approach further improves the selected features by exploiting a page ranking based algorithm to filter non-important processes running on each VM. Once the metric features are defined, a two-stage VM clustering technique is employed to perform both coarse- and fine-grain workload characterization. The inter-cluster and intra-cluster similarity metrics of the silhouette score is used to reveal distinct VM workload groups, as well as the ones with significant overlap. The proposed framework can provide a detailed vision of the underlying behavior of the running VMs. This can assist infrastructure administrators in efficient resource management, as well as root cause analysis.