A five year perspective of traffic pattern evolution in a residential broadband access network

In this paper we describe a systematic study on long-term evolution of residential broadband Internet traffic covering 5 calendar years from June 2007 to May 2011. The traffic evolution is characterized both in the term of the total traffic volume, as well as the traffic volumes and shares for different application categories (file sharing, video streaming etc.), with the focus on comparing the traffic on the per IP user basis and among different broadband subscription groups. The results show that the average daily total traffic generated by each private end user increased only by about 33 % during the past 5 years. Further, the results show that the P2P filesharing has been dominating the network total traffic, but the daily file-sharing traffic volume per end user largely remains the same. Also, the daily streamingmedia traffic volume per end user has increased dramatically by over 500% during the studied period of time. In the meantime, the daily web-browsing traffic volume per end user has increased by about 300%. Finally, a further investigation among 4 different FTTH broadband subscription groups with 1, 10 , 30, and 100 Mbit/s symmetric access speeds shows that the lower the access speed, the more diversified the end user traffic tend to be

Probability density functions of the packet length for computer networks with bimodal traffic

ABSTRACT The research on Internet traffic classification and identification, with application on prevention of attacks and intrusions, increased considerably in the past years. Strategies based on statistical characteristics of the Internet traffic, that use parameters such as packet length (size) and inter-arrival time and their probability density functions, are popular. This paper presents a new statistical modeling for packet length, which shows that it can be modeled using a probability density function that involves a normal or a beta distribution, according to the traffic generated by the users. The proposed functions has parameters that depend on the type of traffic and can be used as part of an Internet traffic classification and identification strategy. The models can be used to compare, simulate and estimate the computer network traffic, as well as to generate synthetic traffic and estimate the packets processing capacity of Internet routers KEYWORD