Abstract-Peer-to-peer relaying is commonly used in realtime applications to cope with NAT and firewall restrictions and provide better quality network paths. As relaying is not natively supported by the Internet, it is usually implemented at the application layer. Also, in a modern operating system, the processor is shared, so the receive-process-forward process for each relay packet may take a considerable amount of time if the host is busy handling some other tasks. Thus, if we happen to select a loaded relay node, the relaying may introduce significant delays to the packet transmission time and even degrade the application performance.In this work, based on an extensive set of Internet traces, we pursue an understanding of the processing delays incurred at relay nodes and their impact on the application performance. Our contribution is three-fold: 1) we propose a methodology for measuring the processing delays at any relay node on the Internet; 2) we characterize the workload patterns of a variety of Internet relay nodes; and 3) we show that, serious VoIP quality degradation may occur due to relay processing, thus we have to monitor the processing delays of a relay node continuously to prevent the application performance from being degraded.
Abstract-The demand for effective VoIP and online gaming traffic management methods continues to increase for purposes such as QoS provisioning, usage accounting, and blocking VoIP calls or game connections. However, identifying such flows has become a significant administrative burden because many of the applications use proprietary signaling and transport protocols. The question of how to identify proprietary VoIP traffic has yet to be solved.In this paper, we propose using a deviation-based classifier to identify VoIP and gaming traffic, given that such real-time interactive services normally send out constant-packet-rate (CPR) traffic with a fixed interval, in order to maintain real-timeliness and interactivity. Our contribution is two-fold: 1) We show that scale-free variability measures are more appropriate than scaledependent ones for quantifying the network variability injected into CPR traffic. 2) Our proposed classifier is particularly lightweight in that it only requires a few inter-packet times to make a decision. The evaluation results show that by only analyzing 10 successive inter-packet times, we can distinguish between CPR and non-CPR traffic with approximately 90% accuracy.
This study presents the first analysis of h-index sequences on a larger scale. Exemplarily, we investigated researchers from three different fields within Computer Science. We use Google Scholar citation profiles as data source to construct the h-index sequences of individual researchers. Our ultimate goal is to develop a self-evaluation tool, to assess one's own development of the h-index in comparison to other researchers in the same field, maybe identify career role models in the field and assess career development with future chances of success. The results of this study show that the average h-index sequences behave differently for the datasets, which is partly due to the different sample sizes. Hence, further research will be needed to confirm if every research field behaves differently. In addition, we applied the algorithm developed by Wu et al. [22] to our data to classify the h-index sequences of individual authors according to five different shape categories. The majority of researchers has an S-shaped h-index sequence, followed by IS-shaped and linear sequences. Purely concave or convex sequences hardly ever occur. The researchers with the highest h-indices after 10 career years respectively belong to the S-shaped and IS-shaped categories with a few linear category occurrences. Hence, having a linear h-index is not only very hard to achieve, it is also not a guaranty to be the researcher with the highest h-index in a field.
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