Broadband User Behavior Characterization
Humberto T. Marques Neto (Federal University of Minas Gerais (UFMG), Brazil), Leonardo C.D. Rocha (Federal University of Minas Gerais (UFMG), Brazil), Pedro H.C. Guerra (Federal University of Minas Gerais (UFMG), Brazil), Jussara M. Almeida (Federal University of Minas Gerais (UFMG), Brazil), Wagner Meira Jr. (Federal University of Minas Gerais (UFMG), Brazil) and Virgilio A.F. Almeida (Federal University of Minas Gerais (UFMG), Brazil)
Copyright: © 2008
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, basically. We also analyzed and classified the e-business services most commonly accessed by users, which did not vary significantly across the user categories.
Key Terms in this Chapter
Session Duration: Provides temporal information about the workload generated by users and estimates how long a user is connected.
Session Arrival Process: Provides temporal information about the workload generated by users and estimates how frequently users start a new session.
Characterization: Process based on actual measures (i.e., access logs) that aims to understand the nature and characteristics of a given scenario, such as the exploration of users’ Internet access data to understand the interaction between users and Internet service providers.
Traffic Volume: Provides leverage on how users are using their connection regarding a critical resource for any ISP: bandwidth.
CBMG: Customer behavior model graph is a state transition graph that has one node for each possible service and transitions between these services; a probability is assigned to a transition between two services representing the frequency at which the user requested the services consecutively in the session.
Service Request Pattern: Is characterized in terms of the frequency of requests to each service and the frequency at which a user switches between different services, within the same session; it is represented with a CBMG.
User Behavior: The behavior of users is defined as a function of the way users arrive at the ISP, how long they remain online, the number of bytes they transfer, and what they do while connected, that is, the request pattern within a session.