This study develops PureRank, a parameter-free importance measure for network nodes based on the recursive definition of importance (RDI). For every directed network, PureRank uniquely determines an importance score vector without user-specified parameters. PureRank can thus provide a neutral reference for parameter-dependent importance measures. PureRank is constructed in three steps: (i) nodes are classified into recurrent , transient , and dangling classes via strongly connected component decomposition; (ii) for each class, the local importance vector is obtained by choosing the parameters of the Katz equation on the class-restricted subnetwork according to the RDI principle; and (iii) the local importance vectors are aggregated into the PureRank vector. This modular design supports parallel and incremental computation while retaining a unified random-surfer interpretation. Numerical experiments on three SNAP networks show that PageRank has a computational advantage over PureRank except when the damping factor is close to one, and that the similarity of PageRank to PureRank depends on and the node classification. In the fully recurrent network, similarity increases monotonically with and reaches Kendall’s and Pearson correlation coefficient at , whereas in the two transient-dominated networks, similarity varies nonmonotonically with . PureRank is extended to multi-attribute networks. • PureRank is a parameter-free recursive importance measure for directed networks. • PureRank provides a neutral reference for parameter-dependent measures. • PureRank’s modular design enables parallel and incremental computation. • PureRank admits a random-surfer interpretation from a Markov chain perspective. • The splitting-network construction extends PureRank to multi-attribute networks.

This paper considers the stationary distribution of the age of information (AoI) in information update systems. We first derive a general formula for the stationary distribution of the AoI, which holds for a wide class of information update systems. The formula indicates that the stationary distribution of the AoI is given in terms of the stationary distributions of the system delay and the peak AoI. To demonstrate its applicability and usefulness, we analyze the AoI in single-server queues with four different service disciplines: first-come first-served (FCFS), preemptive last-come first-served (LCFS), and two variants of non-preemptive LCFS service disciplines. For the FCFS and the preemptive LCFS service disciplines, the GI/GI/1, M/GI/1, and GI/M/1 queues are considered, and for the non-preemptive LCFS service disciplines, the M/GI/1 and GI/M/1 queues are considered. With these results, we further show comparison results for the mean AoI’s in the M/GI/1 and GI/M/1 queues under those service disciplines.

We consider the stationary distributions of the age of information (AoI) and the peak AoI in information update systems. We first derive an invariant relation among the distributions of the AoI, the peak AoI, and the system delay, which holds for a wide class of information update systems. Based on this result, we next obtain several formulas for the stationary distributions of the AoI and the peak AoI in the first-come first-served (FCFS) GI/GI/1 queue, which is a general model of FCFS information update systems. Finally, we derive explicit formulas for the Laplace-Stieltjes transforms of the stationary distributions of the AoI and the peak AoI in FCFS M/GI/1 and GI/M/1 queues.

Software Defined Networking (SDN) is the latest network architecture that does for networking what virtualisation did for servers in data centres. In SDN, separation of the control plane from the data plane brought about new flexibility in the routing of flows through the network. Closely associated with SDN is OpenFlow, the most widely used protocol governing the information exchange between the data plane (switching devices) and the control plane (controller). The ease of implementing and testing new schemes in SDN has prompted many researchers to adopt the experimental and prototyping approach to validate their ideas. Consequently, there has been very little work done to evaluate the performance of SDN and/or OpenFlow-based networks analytically. While the experimentation approach in validation has merits, analytical modelling provides valuable insights by making explicit the dependence of SDN performance on chosen parameters. In this paper: (i) we propose a queueing model of an OpenFlow-based SDN that takes into account classful treatment of packets arriving at a switch and (ii) derive an exact analysis of the proposed queueing model.

IEEE 802.11-based multihop wireless networks have attracted considerable attention for next-generation networks since it can extend the service coverage area without difficulty. However, it is well known that the effective end-to-end throughput (E2ET) degrades due to the overhead caused by the distributed coordination function (DCF) scheme of IEEE 802.11 MAC protocol. To improve this issue, the transmission opportunity (TXOP) mechanism is defined in the IEEE 802.11e standard, with which a wireless node can transmit multiple frames consecutively for a maximum channel occupancy time, called TXOPlimit. This paper considers the performance of the TXOP mechanism for multihop wireless networks. Focusing on a three-node chain topology, we model it as a tandem queueing network with two nodes. The E2ET is derived and the analysis is validated by simulation. Numerical results show that the TXOP mechanism works well for multihop wireless networks. It is also shown that adjusting TXOPlimit is significantly important in order to increase the overall throughput.

This paper considers the characteristics of consecutive block loss for streaming services in which a block for a streaming service consists of original data packets and redundant ones generated by forward error correction (FEC). We consider a single-server queueing system with two input processes: a general renewal input process and a Poisson one. The former represents a streaming-packet flow, while the latter is for background traffic multiplexed at the queue. We investigate the dynamics of the system state during the interval from the arrival epoch of the first packet of the block to that of the last one, deriving the block-loss run distribution, i.e., the probability distribution of the number of consecutive block losses. The analytical model is validated by trace-driven simulation experiments. Numerical examples show that the moments of the distribution are significantly affected by the volume of background traffic and the FEC redundancy, rather than the system capacity.

Network coding is expected to improve throughput performance of multihop wireless networks. However, the throughput performance is significantly affected by the coding overhead at intermediate nodes. In this paper, we consider the trade-off between the throughput and coding overhead. Focusing on an intermediate node of a three-node chain topology, we model it as a single-server queueing system with two buffers. The per-flow throughput is analyzed with a continuous-time Markov chain, and the analysis is validated by simulation. Numerical results show that the analytical results agree fairly well with simulation when the offered loads of the flows are the same. It is also shown that a long processing time for coding causes a significant reduction in throughput.