Optimal-Coherent Network Inference (OCNI): Principles and Applications

Abstract

Many network inference (NI) problems are modeled as under-determined linear inverse problems where the number of observations or measurements are less than the number of unknowns. In this paper, a new technique for solving NI problems in dynamic network environments is presented. This technique is called optimal-coherent NI (OCNI) and it is applied in two stages. In the first stage, called learning phase, the optimal observation matrix (OOM) of network measurements are computed. In the second stage, called measurement and inference phase (MIP), the OOM is used to compute the least-norm solution and estimate the unknowns of interest. The OOM can be adaptively modified to improve the estimation accuracy. In this paper, first, the principles of OCNI are explained, and its properties are mathematically proved and experimentally justified. In addition, a new framework for traffic matrix estimation in software defined networks (SDN) is developed where the OCNI is the main technique for estimating the size of network flows. This framework is called OCcASION. Under the hard resource constraint of the size of ternary content addressable memory (TCAM) in SDN switches, OCcASION mainly uses the readily and reliably available link-load measurements to estimate the size of network flows where link-loads are provided via simple network management protocol (SNMP). In the learning phase, OCcASION computes the OOM of SNMP link-loads. In the MIP, OCcASION adaptively identifies and measures the most informative flows; moreover, it modifies the original OOM and accurately estimate the unknown traffic matrix. For this purpose, OCcASION uses the flexibility provided by the SDN to adaptively re-program a set of TCAM/flow-table entries of OpenFlow switches. The performance of OCcASION framework is evaluated using synthetic and real traffic traces of three practical networks topologies. It is shown that, this framework can significantly improve the accuracy of the traffic matrix estimation. For example, on Geant network the estimation error is approximately reduced by 83%, compared to regular minimum-norm estimation. Furthermore, the principles of OCNI is applied to estimate network link-delays where, in the learning phase, the OOM is computed using the network topology information. In the MIP, a set of path-delay measurements are measured at each measurement interval, and the OOM is used to coherently estimate unknown link-delays.