Measurement-based link dimensioning for the future internet

Network operators have observed a significant increase in traffic demand in the past decade. That is because the Internet is now ubiquitous and provides means to access services essential to our daily life. To accommodate these traffic demands, operators over-provision their networks using simple rules of thumb for link dimensioning. However, throwing more link capacity in the network is not always a viable solution due to operational and financial constraints. Although the amount of link resources will likely not be a problem in the future Internet, the management of these resources will become more important. The current trend on virtualizing services and networks enables us to foresee how virtualization will soon dominate the Internet. Network operators will still own most of the physical infrastructure, but end users will be directly connected to companies that control essential online services and retain users’ content. These companies are often referred to as the Internet big players. Virtual networks will enable transparent and seamless connection between end users and big players. The coexistence of many virtual networks on top of a single physical infrastructure will push for more sophisticated approaches to fairly share and allocate network resources. Efficient and accurate link dimensioning approaches can certainly make the difference in this context. Such approaches can (i) support operators on the optimal allocation of their link resources, while (ii) ensuring that Quality of Service metrics agreed with the big players are met, ultimately (iii) providing end users with good levels of Quality of Experience. Focusing on proper allocation of link resources in the future Internet, in this thesis we develop and validate approaches for link dimensioning that are easy-to-use and accurate. Our starting point is an accurate and already validated dimensioning formula from previous works, which requires traffic statistics that can be calculated from continuous packet captures. However, packet captures are expensive and often demand dedicated hardware/software. Our approaches are able to estimate needed traffic statistics from coarser measurement data, namely sampled packets and flow-level measurements. Technologies able to provide us with such measurement data are largely available in network devices nowadays, namely sFlow, NetFlow/IPFIX and the more recent OpenFlow. The main contributions of this thesis can be divided in three parts. The dimensioning formula we use is built upon the assumption of Gaussian traffic. In the past few years the advent of new online services, from social networking to online storage and video streaming, reshaped the behavior of network users. Past works that assessed Gaussian character of traffic relied on data measured relatively long ago, before these new services became highly popular. Therefore, our first contribution is an extensive investigation of the Gaussian character of current network traffic. We show that the assumption of Gaussian traffic remains valid and, hence, the dimensioning formula is still applicable to today’s traffic. Moreover, in contrast to conclusions from previous works, we proved that traffic Gaussianity is closely related to measured traffic rates and independent of the number of simultaneously active hosts. Aiming at ease of use, our proposed approaches for link dimensioning use data measured with largely available technologies in today’s network devices. These technologies provide coarser data than plain packet captures, but also give us much more information than, e.g., interface counters. As the second contribution of this thesis, therefore, we develop and validate approaches to estimate traffic statistics needed for the dimensioning formula from coarser traffic measurement data. In particular, we develop approaches to estimate traffic statistics from sampled packets obtained from sFlow, or similar packet sampling tools. These approaches account for the missing information (i.e., skipped packets) and the random nature of the sampling algorithms. We also propose approaches that overcome the problem of data aggregation in flow-level measurements from NetFlow/IPFIX, or similar tools. To estimate the needed traffic statistics from flows, these flow-based approaches account for the missing information on individual packets. The proposed approaches in this thesis are able to accurately estimate required capacity at timescales from milliseconds to seconds. Finally, the recent Software-Defined Networking (SDN) architecture claims to be ideal for managing dynamic network applications. OpenFlow is the best known enabler of SDN and it is already widely available in network devices. Although OpenFlow is primarily a traffic forwarding technology, in theory, it can also measure flow data as needed by our flow-based link dimensioning approaches (i.e., NetFlow/IPFIX style). In practice, however, measured data from current implementations of OpenFlow are of poor quality. As the third contribution of this thesis, we introduce an approach to retrieve measured data from the OpenFlow switch, using the OpenFlow protocol, for purposes of link dimensioning. In addition, we assess the quality of measured data from OpenFlow both in a physical setup, using a real OpenFlow switch, and in a virtual setup, running a commonly used open source OpenFlow implementation. Results collected from our experiments lead us to conclude that measured data in OpenFlow is not yet suitable for link dimensioning.