Evaluation of Network Topology Inference in Opaque Compute Clouds through End-to-End Measurements

Modern Infrastructure-as-a-Service (IaaS) clouds offer an unprecedented flexibility and elasticity in terms of resource provisioning through the use of hardware virtualization. However, for the cloud customer, this virtualization also introduces an opaqueness which imposes serious obstacles for data-intensive distributed applications. In particular, the lack of network topology information, i.e. information on how the rented virtual machines are physically interconnected, can easily cause network bottlenecks as common techniques to exploit data locality cannot be applied. In this paper we study to what extent the underlying network topology of virtual machines inside an IaaS cloud can be inferred based on end-to-end measurements. Therefore, we experimentally evaluate the impact of hardware virtualization on the measurable link characteristics packet loss and delay using the popular open source hypervisors KVM and XEN. Afterwards, we compare the accuracy of different topology inference approaches and propose an extension to improve the inference accuracy for typical network structures in datacenters. We found that common assumptions for end-to-end measurements do not hold in presence of virtualization and that RTT-based measurements in Para virtualized environments lead to the most accurate inference results.