Virtual-to-Physical Mapping Inference in Virtualized Cloud Environments

Virtualization is a double-edged sword in large scale cloud computing environments. On one hand, virtualization provides a logical and unified view of the underlying cloud resources to facilitate more efficient resource utilization and to support multi-tenancy. On the other hand, virtualization introduces additional layers of indirections which make the virtual-to-physical resource mapping relationship obscure, or prohibitively costly to unveil, especially with multiple layers of virtualization at presence, e.g., server virtualization, network virtualization, and storage virtualization. In this work, we propose a virtual-to-physical mapping inference framework by analyzing the I/O activities of inputs and outputs of the virtualization layer. Using a virtualized storage example, we show that our lightweight solution can unveil the virtual-to-physical resource mapping relationship in a non-intrusive fashion, which can be used in many cloud management tasks such as root cause analysis and diagnosis.