Smart partitioning of geo-distributed resources to improve cloud network performance

Cloud Computing systems with geo-distributed resources are becoming more popular for enabling a new family of applications, which are latency sensitive or bandwidth intensive, e.g., Internet of Things and online video gaming services. The approach is to host the cloud services at the network edges to reduce the latency and bandwidth consumption. However, the topology of the existing networks is not necessarily optimal for hosting Cloud services. Moreover, how the services are placed on the nodes, can affect the performance of the applications and the whole network. Therefore, we propose a novel algorithm to partition a distributed infrastructure into a set of computing clusters, each called a Micro Data Center. Our proposed algorithm is a decentralized community detection algorithm that does not require any global knowledge of the network topology. We compare our solution with a geolocation based clustering solution and demonstrate our preliminary results based on a real world network data set. We show that micro data centers increase the minimum available bandwidth in the network to up to 62%. Likewise, the average latency can be reduced to 50%.