The coexistence of multiple types of interactions within social, technological, and biological networks has motivated the study of the multilayer nature of real-world networks. Meanwhile, identifying network structures from dynamical observations is an essential issue pervading over the current research on complex networks. This paper addresses the problem of structure identification for multilayer networks, which is an important topic but involves a challenging inverse problem. To clearly reveal the formalism, the simplest two-layer network model is considered and a new approach to identifying the structure of one layer is proposed. Specifically, if the interested layer is sparsely connected and the node behaviors of the other layer are observable at a few time points, then a theoretical framework is established based on compressive sensing and regularization. Some numerical examples illustrate the effectiveness of the identification scheme, its requirement of a relatively small number of observations, as well as its robustness against small noise. It is noteworthy that the framework can be straightforwardly extended to multilayer networks, thus applicable to a variety of real-world complex systems.

Datacenter networks suffer unpredictable performance due to a lack of application level bandwidth guarantees. A lot of attentions have been drawn to solve this problem such as how to provide bandwidth guarantees for Virtualized Machines (VMs), proportional bandwidth share among tenants, and high network utilization under peak traffic. However, existing solutions fail to cope with highly dynamic traffic in datacenter networks. In this paper, we consider the effects of large numbers of short flows and massive bursty traffic in the datacenter, and design a novel distributed rate allocation algorithm based on the Logistic model under the control-theoretic framework. The theoretical analysis and experimental results using OpenFlow show that our algorithm not only guarantees the bandwidth for VMs, but also provides fast convergence to efficiency and fairness, and smooth response to bursty traffic.

In this study, the optimal power allocation (OPA) problem is studied for the two‐way relay channel employing the analogue network coding protocol with the asymmetric traffic requirements in a cognitive radio network. The OPA scheme is proposed by maximising the sum‐rate of the success delivery bit from the secondary user (SU) perspective subject to the sum‐power constraint and the interference power threshold (IPT) constraints to the primary user (PU). In particular, for the purpose of accomplishing the power allocation more realistically, the interference to each other between the PU and the SU and the relay is taken into consideration and by solving the optimisation problem composed for each case, the closed form expressions of each node power are obtained. All the simulation results have demonstrated that the system with the proposed scheme outperforms the equal power allocation (EPA) scheme irrespective of the IPT. When the IPT constraint is introduced, the sum‐rate of the success delivery bit of the SU communication converges to a certain level with the increasing total power. Meanwhile, the proposed strategy is superior to the conventional EPA scheme for any relay location, which is more remarkable when the relay is located on either end node regardless of the traffic symmetry or the asymmetry.