Inferring Network Topologies in Infrastructure as a Service Cloud

Abstract

Infrastructure as a Service (IaaS) clouds are gaining increasing popularity as a platform for distributed computations. The virtualization layers of those clouds offer new possibilities for rapid resource provisioning, but also hide aspects of the underlying IT infrastructure which have often been exploited in classic cluster environments. One of those hidden aspects is the network topology, i.e. the way the rented virtual machines are physically interconnected inside the cloud. We propose an approach to infer the network topology connecting a set of virtual machines in IaaS clouds and exploit it for data-intensive distributed applications. Our inference approach relies on delay-based end-to-end measurements and can be combined with traditional IP-level topology information, if available. We evaluate the inference accuracy using the popular hyper visors KVM as well as XEN and highlight possible performance gains for distributed applications.