In the paper, we consider a polling system with a cyclic adaptive polling order. The server polls the queues in a cyclic way but skips (does not visit) those that were empty when polling them in the previous cycle. We apply the generating function method to derive first and second order moments of the stationary state distribution of the queue length at the polling moments that allows calculating the mean delay in queues. Also we provide numerical results.

Mobile broadband (MBB) and ubiquitous machine type communications (MTC) appear essential in future 5G networks, however their requirements in terms of data rate, expected number of users, latency and reliability are largely contradictory, which constitutes a major challenge for 5G architecture design. MTC data aggregation via device clustering may prevent signaling congestion due to a massive number of MTC devices in the cell. Resource sharing between MBB and MTC sessions with MTC data aggregation can be modeled using a multi-service loss system, however its numerical analysis is impeded due to the considerable difference in scales of MBB and MTC parameters. In the paper, we use simulation to assess the performance measures of the loss system and its more realistic modification. Cases of light, moderate and heavy MTC loading and various parameters of the MMPP of MTC arrivals are considered.

In this paper we assume that the customers arrive at a single server queueing system according to Markovian Arrival process. When the system is empty, the server goes for vacation and produces inventory for future use during this period. The maximum processed inventory at a stretch isL. The inventory processing time follows phase type distribution. These are required for the service of customers-one for each customer. The server returns from vacation when there are N customers in the system. The service time follows two distinct phase type distributions depending on whether there is processed items or no processed item available at service commencement epoch. We analyse the distributions of time till the number of customers hit N or the inventory level reaches L, idle time, the distribution of time until the number of customers hit N and also the distribution of the number of inventory processed before the arrival of first customer. Also we provide the distribution of a busy cycle, LSTs of busy cycles in which no item is left in the inventory and at least one item is left in the inventory. We perform some numerical experiments to evaluate the expected idle time, standard deviation and coefficient of variation of idle time of the server.

The article presents the results of the analysis of the need for unification of tethered telecommunication high-altitude platforms (HAP). Possible ways of unification and standardization of HAP, as well as criteria and methods of their structural optimization are considered. It is shown that one of the methods for increasing the efficiency of telecommunication networks is the unification of requirements, component parts and their composite systems. The requirements that should be laid at the stage of setting a technical task for product development are given and it is shown that such parameters as the repeatability factor, the applicability factor, the inter-project unification coefficient are used to assess the level of unification. It is shown that to solve the problems of hardware-software unification and ensure information compatibility, it is necessary to develop appropriate models that take into account the specifics of modern information transmission systems in HAP. The rationale for using the apparatus of logical-dynamic models for solving unification problems is proved, and their joint use with network models allows to formalize the information necessary for solving the problems of hardware-software unification of HAP and ensuring their information compatibility.

We propose the new EI-clustering method for random networks. Regarding the underlying graph of a random network, EI-clustering is an advanced statistical tool for community detection and based on the estimation of the extremal index (EI) associated with each node. The EI metric is estimated by samples of indices of the node in uences. The latter quantities are determined by the PageRank and a Max-Linear Model. The EI values of both models are estimated by a blocks estimator for each node which is considered as the root of a Thorny Branching Tree. Generations of descendant nodes related to the root node of the tree are used as blocks. The reciprocal of the EI value indicates the average number of in uential nodes per generation containing at least one in uential node. In the context of random graphs the EI metric indicates the ability of a randomly selected node to attract highly ranked nodes in its orbit. Looking at the changing shape of a plot of the EI metric versus the node number, the node communities are detected. The EI-clustering method is compared with the conductance measure regarding the data set of a real Web graph.