Measurement-Based Network Link Dimensioning

The ever increasing traffic demands and the current trend of network and services virtualization calls for effective approaches for optimal use of network resources. In the future Internet multiple virtual networks will coexist on top of the same physical infrastructure, and these will compete for bandwidth resources. Link dimensioning can support fair share and allocation of bandwidth. Current approaches however, are ineffective at smaller timescales or require traffic measurements that are not easy to obtain. In this thesis we focused on easy to deploy and accurate link dimensioning approaches for the future Internet. The start point of our work is a dimensioning formula, proposed in 2006, built upon the assumption of Gaussian traffic. This formula is able to accurately estimate required capacity at very small timescales. To do so it requires traffic statistics that can be obtained from packet captures. The contribution of this thesis is threefold. First, we prove that the assumption of Gaussian traffic holds for current Internet traffic and, hence, the dimensioning formula can still be applied. Second, instead of relying on costly packet captures, we develop and validate link dimensioning approaches that estimate the needed traffic statistics from measurement data obtained via technologies that are largely found in today's networks (namely, sFlow and NetFlow/IPFIX). Our approaches are able to accurately estimate required capacity at timescales as low as 1ms. Last, we propose a link dimensioning approach that uses measured data from the recent and already widely available OpenFlow. We also investigate the quality of flow-level measurements in current implementations of OpenFlow, and demonstrate that these are not yet accurate enough for link dimensioning purposes.