We study layered queueing systems comprised two interlacing finite M/M/• type queues, where users of each layer are the servers of the other layer. Examples can be found in file sharing programs, SETI@home project, etc. Let Li denote the number of users in layer i, i=1, 2. We consider the following operating modes: (i) All users present in layer i join forces together to form a single server for the users in layer j (j≠i), with overall service rate μjLi (that changes dynamically as a function of the state of layer i). (ii) Each of the users present in layer i individually acts as a server for the users in layer j, with service rate μj.

These operating modes lead to three different models which we analyze by formulating them as finite level-dependent quasi birth-and-death processes. We derive a procedure based on Matrix Analytic methods to derive the steady state probabilities of the two dimensional system state. Numerical examples, including mean queue sizes, mean waiting times, covariances, and loss probabilities, are presented. The models are compared and their differences are discussed.

Mobile networks are universally used for personal communications, but also increasingly used in the Internet of Things and machine-to-machine applications in order to access and control critical services. However, they are particularly vulnerable to signaling storms, triggered by malfunctioning applications, malware or malicious behavior, which can cause disruption in the access to the infrastructure. Such storms differ from conventional denial of service attacks, since they overload the control plane rather than the data plane, rendering traditional detection techniques ineffective. Thus, in this paper we describe the manner in which storms happen and their causes, and propose a detection framework that utilizes traffic measurements and key performance indicators to identify in real-time misbehaving mobile devices. The detection algorithm is based on the random neural network which is a probabilistic computational model with efficient learning algorithms. Simulation results are provided to illustrate the effectiveness of the proposed scheme.