Cloud computing systems require a placement logic that decides where to allocate resources. In state-of-the-art platforms such as OpenStack, this scheduler takes into account multiple constraints when starting a new instance, including in particular the required computational and memory resources. However, this scheduling mechanism typically neither considers network requirements of Virtual Machines nor the networking resources that are actually available. In this paper we present an extension of the OpenStack scheduler that enables a network-aware placement of instances by taking into account bandwidth constraints to and from nodes. Our solution keeps track of host-local network resource allocation, and it can be combined with bandwidth enforcement mechanisms such as rate limiting. We present a prototype that requires only very few changes in the OpenStack open source software. Testbed measurement results demonstrate the benefit of our solution compared to the OpenStack default approach.

The ability to extract topology information from the network is important for many applications and enables more informed resource selection. The challenge for topology exposure is to provide a compact representation that is sufficiently accurate and complies to topology hiding policies. This paper presents a topology abstraction system that can expose large-scale service provider network maps with an adjustable level-of-detail. Our system uses graph sparsification algorithms to reduce the complexity of routing topologies. Our numerical results reveal that the size of maps can be reduced by one order of magnitude or more while the result still enables reasonable traffic optimization inside applications. A proof-of-concept implementation gathers network management system data and exposes abstract maps through the Application-Layer Traffic Optimization (ALTO) protocol.

We consider a system of compute and storage resources geographically distributed over a large number of locations connected via a wide-area network. By distributing the resources, latency to users can be decreased, bandwidth costs reduced and availablility increased. The challenge is to distribute services with varying characteristics among the data centers optimally. Some services are very latency sensitive, others need vast amounts of storage, and yet others are computationally complex but do not require hard deadlines on execution. We propose efficient algorithms for the placement of services to get the maximum benefit from a distributed cloud systems. The algorithms need input on the status of the network, compute resources and data resources, which are matched to application requirements.