Efficient performance monitoring for ubiquitous virtual networks based on matrix completion

Inspired by the concept of software-defined network and network function virtualization, vast virtual networks are generated to isolate and share wireless resources for different network operators. To achieve fine-grained resource control and scheduling among virtual networks (VNs), network performance monitoring is essential. However, due to limitation of hardware, real-time performance monitoring is impossible for a complete virtual network. In this paper, taking advantage of the low-rank characteristic of 90 virtual access points (VAPs) measurement data, we propose an intelligent measurement scheme, namely, adaptive and sequential sampling based on matrix completion (MC), which exploits from the MC to construct the complete data of VN performance from a partial direct monitoring data. First, to construct the initial measurement matrix, we propose a sampling correction model based on dispersion and coverage. Second, a stopping condition for the sequential sampling is introduced, based on the stopping condition, the sampling process for a period can stop without waiting for the matrix reconstruction to reach certain of accuracy level. Finally, the sampled VAPs are determined by referring the back-forth completed matrixes\u27 normalized mean absolute error. The experiments show that our approach can achieve a constant network perception and maintain a relatively low error rate with a small sampling rate

Traffic matrix estimation enhanced by SDNs nodes in real network topology

Traffic matrix estimation in communication networks is challenging problem, whose solution provides a valuable management and planning tool. Given the range of technologies able to reconfigure the resource assignment, real-time knowledge of the traffic matrix enables smart adaptive traffic management functions. A new perspective is given to the traffic matrix estimation problem by the Software Defined Network (SDN) concept. We investigate an evolutionary approach, where SDN nodes are introduced into a traditional IP network, to understand how their new capabilities affect the statement and accuracy of the traffic matrix estimation problem. By referring to operational networks and benchmark measured data, we show that a major boost of estimate accuracy can be obtained with very few SDN nodes, performing very simple tasks. To that end we develop an underlying theory that helps locating SDN functionalities in the most convenient way