-In this paper we consider the connection between the packet loss ratio (PLR) in a switch with a finite buffer of size L and the tail distribution of the corresponding infinite buffer queue Q. In the literature the PLR is often approximated with the tail probability P(Q > L), and in practice the latter is often a good conservative estimate on the PLR. Therefore, efforts have mainly focused on finding bounds and asymptotic expressions concerning the tail probabilities of the infinite queue. However, our first result shows that the ratio PLR/P(Q > L) can be arbitrary, in particular the PLR can be larger than the tail probability. We also determine an upper bound on this ratio yielding an upper bound on the PLR using the tail distribution of the infinite queue. The bound is fairly tight for certain traffic patterns. In many situations it clearly improves the estimation with the tail probability, and it is rarely significantly larger than the estimate P(Q > L), while it is an upper bound. On the other hand, if the PLR is much smaller than P(Q > L), then our bound is usually loose. For this case a practically good approximation on their ratio is proposed.
Detailed knowledge about the traffic mixture is essential for network operators and administrators, as it is a key input for numerous network management activities. Several traffic classification approaches co-exist in the literature, but none of them performs well for all different application traffic types present in the Internet. In this study we compare and benchmark the currently known traffic classification methods on network traces captured in an operational 3G mobile network. Utilizing the experiences about the strengths and weaknesses of the existing approaches, a novel combined method is proposed aiming at improving the completeness and accuracy of classification. The novel method is based on a complex decision mechanism, which can provide appropriate identification for each different application type. As a main contribution, with the help of the new method it is shown that applications previously used only in fixed access networks may appear in mobile broadband environment.
Spiking neural networks (SNN) are expected to enable several use-cases in future communication networks (beyond 5G and 6G), as edge AI and battery-constrained systems can leverage the fast computation and high-power efficiency offered by SNNs. In this work we consider a Distributed Wireless SNN (DW-SNN) system and we analyze its performance in terms of inference accuracy and total neural activity when radio losses are applied to spikes transferred during the inference phase. Our aim is to understand how radio losses impact performance when considering different SNN spike communication types, i.e., input, excitatory, and inhibitory spikes. Then we evaluate the impact of different traffic prioritization approaches among SNN spikes when considering a shared channel capacity being available for SNN activity. From these analyses, we derive some key insights and features that can be considered when applying a DW-SNN and handling its traffic over wireless communication systems. Finally, we report a prototype implementation of DW-SNN using custom-built IoT components, which we use to further investigate different coverage scenarios.
Video traffic compressed with variable bit rate coding scheme is known to possess high variations and multiple time scale characteristics. This property makes parsimonious video modeling a difficult task. A possible way of describing this traffic is via self-similar models, which also produce high variations on many time scales. However, these are general traffic models and do not represent many important characteristics of video. In this paper we show that video traffic has well-separable time scales. Based on this result, a new model is presented, which is capable of capturing the main properties of VBR video. The concept is scene-oriented, while a larger time scale-called epoch-is introduced. The main contribution of this paper is that the presence of multiple time scales seem to be the real reason for the slowly decaying autocorrelation function rather than heavy tailed level durations. Finally, the application of the model is shortly discussed for dimensioning, admission control and simulation purposes.
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