A genetic approach to Markovian characterisation of H.264 scalable video

We propose an algorithm for multivariate Markovian characterisation of H.264/SVC scalable video traces at the sub-GoP (Group of Pictures) level. A genetic algorithm yields Markov models with limited state space that accurately capture temporal and inter-layer correlation. Key to our approach is the covariance-based fitness function. In comparison with the classical Expectation Maximisation algorithm, ours is capable of matching the second order statistics more accurately at the cost of less accuracy in matching the histograms of the trace. Moreover, a simulation study shows that our approach outperforms Expectation Maximisation in predicting performance of video streaming in various networking scenarios

On the statistical description of the inbound air traffic over Heathrow airport

We present a model to describe the inbound air traffic over a congested hub. We show that this model gives a very accurate description of the traffic by the comparison of our theoretical distribution of the queue with the actual distribution observed over Heathrow airport. We discuss also the robustness of our model

Supporting real time video over ATM networks

Includes bibliographical references.In this project, we propose and evaluate an approach to delimit and tag such independent video slice at the ATM layer for early discard. This involves the use of a tag cell differentiated from the rest of the data by its PTI value and a modified tag switch to facilitate the selective discarding of affected cells within each video slice as opposed to dropping of cells at random from multiple video frames

Compact Markov-modulated models for multiclass trace fitting

Markov-modulated Poisson processes (MMPPs) are stochastic models for fitting empirical traces for simulation, workload characterization and queueing analysis purposes. In this paper, we develop the first counting process fitting algorithm for the marked MMPP (M3PP), a generalization of the MMPP for modeling traces with events of multiple types. We initially explain how to fit two-state M3PPs to empirical traces of counts. We then propose a novel form of composition, called interposition, which enables the approximate superposition of several two-state M3PPs without incurring into state space explosion. Compared to exact superposition, where the state space grows exponentially in the number of composed processes, in interposition the state space grows linearly in the number of composed M3PPs. Experimental results indicate that the proposed interposition methodology provides accurate results against artificial and real-world traces, with a significantly smaller state space than superposed processes

Real-time characteristics of switched ethernet for "1553B" -embedded applications : simulation and analysis

In our previous work , Full Duplex Switched Ethernet was put forward as an attractive candidate to replace the MIL-STD 1553B data bus, in next generation "1553B"-embedded applications. An analytic study was conducted, using the Network Calculus formalism, to evaluate the deterministic guarantees offered by our proposal. Obtained results showed the effectiveness of traffic shaping techniques, combined with priority handling mechanisms on Full Duplex Switched Ethernet in order to satisfy 1553B-like real-time constraints. In this paper, we extend this work by the use of simulation. This gives the possibility to capture additional characteristics of the proposed architecture with respect to the analytical study, which was basically used to evaluate worst cases and deterministic guarantees. Hence, to assess the real-time characteristics of our proposed interconnection technology, the results yielded by simulation are discussed and average latencies distributions are considered

Towards Optimal Distributed Node Scheduling in a Multihop Wireless Network through Local Voting

In a multihop wireless network, it is crucial but challenging to schedule transmissions in an efficient and fair manner. In this paper, a novel distributed node scheduling algorithm, called Local Voting, is proposed. This algorithm tries to semi-equalize the load (defined as the ratio of the queue length over the number of allocated slots) through slot reallocation based on local information exchange. The algorithm stems from the finding that the shortest delivery time or delay is obtained when the load is semi-equalized throughout the network. In addition, we prove that, with Local Voting, the network system converges asymptotically towards the optimal scheduling. Moreover, through extensive simulations, the performance of Local Voting is further investigated in comparison with several representative scheduling algorithms from the literature. Simulation results show that the proposed algorithm achieves better performance than the other distributed algorithms in terms of average delay, maximum delay, and fairness. Despite being distributed, the performance of Local Voting is also found to be very close to a centralized algorithm that is deemed to have the optimal performance

Inference for double Pareto lognormal queues with applications

In this article we describe a method for carrying out Bayesian inference for the double Pareto lognormal (dPlN) distribution which has recently been proposed as a model for heavy-tailed phenomena. We apply our approach to inference for the dPlN/M/1 and M/dPlN/1 queueing systems. These systems cannot be analyzed using standard techniques due to the fact that the dPlN distribution does not posses a Laplace transform in closed form. This difficulty is overcome using some recent approximations for the Laplace transform for the Pareto/M/1 system. Our procedure is illustrated with applications in internet traffic analysis and risk theory