Hypervisor Agnostic Workload Characterization of Virtual Machines

Abstract

Workload characterization is an important feature in a cloud environment. Using a fast and accurate characterization cloud providers can allocate virtual machines in physical hosts that best fit a specific workload and improve the overall performance without new investments. Current strategies of workload characterization are based on complex algorithms that are difficult to apply in a cloud environment with thousands of virtual machines running. Other strategies to characterize virtual machines rely on several changes in a hypervisor, or virtual machine layer, and are hypervisordependent. This paper presents a hypervisor agnostic characterization methodology that uses standard metrics of Processor and Memory utilization, available in SNMP. Collected data are normalized and applied to a low computational cost decision tree, that is able to characterize a virtual machine in a customizable time window. As evaluation, some tests were performed in different hypervisors (KVM, Xen and VMWare) running spec benchmark and in real workloads, such as Hadoop Cluster in Rackspace and a production Web Server running in a VMWare Farm. Results showed that our methodology is able to infer a very accurate characterization.

Análise de Duas Estratégias de Caracterização para Computação na Nuvem

“…Além da baixa complexidade, os dois algoritmos compartilham o método de Aprendizagem Supervisionada, isto significa que é necessário submeter os algoritmos a uma massa de dados previamente rotuladas. Para a geração dos dados de aprendizagem, foi utilizado o Isolation Benchmark, já avaliado e utilizado em trabalhos anteriores [12] [18].…”