Monitoring Federated Softwarized Networks: Approaches for Efficient and Collaborative Data Collection in Large-Scale Software-Defined Networks

The term Softwarized Networking encapsulates technologies that allow the use of software to program a communication network. These technologies, predominantly Software-Defined Networking (SDN) and Network Functions Virtualization (NFV), have dominated the scientific interests of the networking community in the last decade. Leading companies already adopted SDN in large-scale deployments (e.g., Google’s B4 Project, Microsoft Azure). According to Cisco, 76% of all data centers will apply SDN by 2021. Along with a hand full of valuable advantages, the foundation of the success of Softwarized Networking lies in its flexibility. In the case of SDN, a logically centralized controller, denoted control-plane, uses software to dynamically change how the networking devices, denoted data-plane, handle traffic. This centralization tremendously eases the management process. With respect to network state monitoring, which is a cornerstone of network management and the basis for its adaptivity, SDN provides, in addition to the advantage of the by-design centrally available knowledge, a set of new techniques to collect statistics from the networking devices. The centralization of the control-plane quickly turned out to be only of logical nature and requires a physically distributed implementation to achieve scalability and reliability. Therefore, numerous distributed controller architectures have been proposed. Yet, the distribution of the control and, in line with this, the distribution of large-scale networks (e.g., one data center consists of a multitude of distributed sub-data centers) have not been considered in existing monitoring approaches. We believe there is a potential to increase the efficiency of monitoring when network parts collaborate. In this thesis, we exploit this potential by developing monitoring approaches that utilize coordination and information exchange among collaborating SDN controllers. We create mechanisms to discover redundancy in the monitoring of shared resources and aggregate overlapping measurement tasks of different controllers whenever possible. Doing so, we substantially cut down the costs for monitoring, which is necessary for future networks that face a vast increase in load and dynamics. On top of this, we zoom into the statistic collection process in Softwarized Networks between controllers and the data-plane devices. Within that, we identify three not yet fully explored aspects, namely how, where, and which statistics to measure from the network. We propose novel methods for these aspects to collect information efficiently while limiting resource consumption. Extensive evaluations show that filtering irrelevant data can reduce the required measurement transmissions to a fraction and an intelligent measurement point placement requires only a small number of measurements compared to measuring the entire network, without affecting the accuracy.