Internet traffic volumes characterization and forecasting

Internet usage increases every year and the need to estimate the growth of the generated traffic has become a major topic. Forecasting actual figures in advance is essential for bandwidth allocation, networking design and investment planning. In this thesis novel mathematical equations are presented to model and to predict long-term Internet traffic in terms of total aggregating volume, globally and more locally. Historical traffic data from consecutive years have revealed hidden numerical patterns as the values progress year over year and this trend can be well represented with appropriate mathematical relations. The proposed formulae have excellent fitting properties over long-history measurements and can indicate forthcoming traffic for the next years with an exceptionally low prediction error. In cases where pending traffic data have already become available, the suggested equations provide more successful results than the respective projections that come from worldwide leading research. The studies also imply that future traffic strongly depends on the past activity and on the growth of Internet users, provided that a big and representative sample of pertinent data exists from large geographical areas. To the best of my knowledge this work is the first to introduce effective prediction methods that exclusively rely on the static attributes and the progression properties of historical values

On the Double-Faced Nature of P2P Traffic

Over the last few years, peer-to-peer (P2P) file sharing applications have evolved to become a major traffic source in the Internet. The ability to quantify their impact on the network, as a consequence of both signaling and download traffic, is fundamental to a number of network operations, including traffic engineering, capacity planning, quality of service, forecasting for long-term provisioning, etc. We present here a measurement study on the characteristics of the traffic associated with different P2P applications. Our aim is to offer useful insight into the nature of P2P traffic, which we consider a step toward building P2P traffic aggregates generators in simulative environments. We show that P2P traffic can be divided into two distinguished behavioral profiles, which, independently of the application protocol, present significant differences in the average and standard deviation of four measurements: arrival times, durations, volumes and average packet sizes ofP2P conversations. These profiles well represent the typical behavior of signaling and download traffic. Based on our findings, we argue that, if such distinction is not taken into account, the statistical measurements needed to model P2P traffic aggregates would result biased, and potentially bring to misleading results