Traffic Patterns on Different Internet Access Technologies (original) (raw)
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Traffic analysis and characterization of Internet user behavior
International Congress on Ultra Modern Telecommunications and Control Systems, 2010
Internet usage has changed, and the demands on the broadband access networks have increased, both regarding bandwidth and QoS. Characterizing the traffic, as seen by a broadband access network, can help understanding both the demands of today and the demands of tomorrow. In this paper we analyze traffic measurements from a Swedish municipal broadband access network and derive corresponding user behavior models. The paper focuses on Internet usage in terms of traffic patterns, volumes and applications. Also, user activity characteristics, as session lengths and traffic rate distributions, are analyzed and modeled. Notably, the resulting models for user session lengths turn out different than traditionally assumed.
Comparison of User Traffic Characteristics on Mobile-Access versus Fixed-Access Networks
Lecture Notes in Computer Science, 2012
We compare Web traffic characteristics of mobile-versus fixed-access end-hosts, where herein the term "mobile" refers to access via cell towers, using for example the 3G/UMTS standard, and the term "fixed" includes Wi-Fi access. It is well-known that connection speeds are in general slower over mobile-access networks, and also that often there is higher packet loss. We were curious whether this leads mobile-access users to have smaller connections. We examined the distribution of the number of bytesper-connection, and packet loss from a sampling of logs from servers of Akamai Technologies. We obtained 149 million connections, across 57 countries. The mean bytes-per-connection was typically larger for fixed-access: for two-thirds of the countries, it was at least onethird larger. Regarding distributions, we found that the difference between the bytes-per-connection for mobileversus fixed-access, as well as the packet loss, was statistically significant for each of the countries; however the visual difference in plots is typically small. For some countries, mobile-access had the larger connections. As expected, mobile-access often had higher loss than fixed-access, but the reverse pertained for some countries. Typically packet loss increased during the busy period of the day, when mobile-access had a larger increase. Comparing our results from 2010 to those from 2009 of the same time period, we found that connections have become a bit smaller.
EMERGING TOPICS IN COMPUTING Characterizing User Behavior in Mobile Internet
Smart devices bring us the ubiquitous mobile accessing to Internet, making mobile Internet grow rapidly. Using the mobile traffic data collected at core metropolitan 2G and 3G networks of China over a week, this paper studies the mobile user behavior from three aspects: 1) data usage; 2) mobility pattern; and 3) application usage. We classify mobile users into different groups to study the resource consumption in mobile Internet. We observe that traffic heavy users and high mobility users tend to consume massive data and radio resources simultaneously. Both the data usage and the mobility pattern are closely related to the application access behavior of the users. Users can be clustered through their application usage behavior, and application categories can be identified by the ways to attract the users. Our analysis provides an comprehensive understanding of user behavior in mobile Internet, which may be used by network operators to design appropriate mechanisms in resource provision and mobility management for resource consumers based on different categories of applications. INDEX TERMS Mobile Internet, network traffic, data usage, mobility pattern, user behavior.
Characterizing broadband user behavior
Proceedings of the 2004 ACM workshop on Next-generation residential broadband challenges - NRBC '04, 2004
This paper presents a characterization of broadband user behavior from an Internet Service Provider standpoint. Users are broken into two major categories: residential and Small-Office/Home-Office (SOHO). For each user category, the characterization is performed along four criteria: (i) session arrival process, (ii) session duration, (iii) number of bytes transferred within a session and (iv) user request patterns.
Broadband User Behavior Characterization
Handbook of Research on Global Diffusion of Broadband Data Transmission
This chapter presents a broadband user behavior characterization from an Internet service provider standpoint. Understanding these user behavior patterns is important to the development of more efficient applications for broadband users. Our characterization divides the users into two categories: residential and small-office/home-office (SOHO). It employs four characterization criteria: session arrival process, session duration, number of bytes transferred within a session, and user request patterns. Our results show that both residential and SOHO session interarrival times are exponentially distributed, and point out that a typical SOHO user session is longer and transfers a larger volume of data. Our analysis also uncovers two main groups of session request patterns within each user category: (i) sessions that comprise traditional Internet services, such as WWW services, e-mail, and instant messenger, and (ii) sessions that comprise peer-to-peer file sharing applications, basicall...
Characterisation of Web traffic
GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270), 2001
In this work the authors show how the behaviour of Web users strongly affects the dynamics of TCP connections in Internet. Analysing actual and systematically generated HTTP traces, it is proved that the time between the download of two pages is critical to determine the re-utilisation of TCP connections. On the other hand, the study also shows that the utilisation of 1.1 version of the HTTP standard does not significantly affect the traffic generated by HTTP 1.0. In this sense, the heavy-tailed nature of the size of HTTP connections can be considered as an invariant property.
Data transfer statistics in internet dial - up sessions
European Transactions on Telecommunications, 2001
This letter reports the results of a measurement campaign aimed at characterizing the traffic in Internet dial—up sessions. This is done at the data transfer level, i. e. considering the average data rate and the volume of data transferred during calls to Internet service providers. Major statistics (mean and two percentile values) are provided for both quantities with a hourly time resolution together with the empirical probability density functions relative to the peak hour.
Volume 13, Issue 3
A traffic generation model is a stochastic model of the data flow in a communication network. These models are useful during the development of telecommunication technologies and for analyzing the performance and capacity of various protocols,algorithms, and network topologies. We present here two modeling approaches for simulating internet traffic. In our models, we simulate the length and interarrival times of individual packets, the discrete unit of data transfer over the internet. Our first modeling approach is based on fitting data to known theoretical distributions. The second method utilizes empirical copulae and is completely data driven. Our models were based on internet traffic data generated by different individuals performing specific tasks (e.g., web browsing, video streaming, and online gaming). When combined, these models can be used to simulate internet traffic from multiple individuals performing typical tasks. KEYWORDS: Internet Traffic Simulation; Stochastic Model...
2019 28th Wireless and Optical Communications Conference (WOCC), 2019
The internet activity records (IARs) of a mobile cellular network posses significant information which can be exploited to identify the network's efficacy and the mobile users' behavior. In this work, we extract useful information from the IAR data and identify a healthy predictability of spatio-temporal pattern within the network traffic. The information extracted is helpful for network operators to plan effective network configuration and perform management and optimization of network's resources. We report experimentation on spatiotemporal analysis of IAR data of the Telecom Italia. Based on this, we present mobile traffic partitioning scheme. Experimental results of the proposed model is helpful in modelling and partitioning of network traffic patterns.