\section{Related Work}
\label{sec:rel}

Workload characterization is fundamental to the understanding and improvement of Web systems. There are various studies which present workload characterizations
of different types, such as Web servers~\cite{arlitt_invariants}, e-commerce~\cite{menasce2000}, blogs~\cite{duarte2007}, video on demand~\cite{krusty04www},
and live video~\cite{eveline@tnet}. Among various contributions of these works, we highlight the creation of valuable models able to describe the workload that 
arrives on these server, essential for synthetic workload generation, which allows experimentation and simulation based on realistic workloads.
Particularly, Costa~\textit{et al.}~\cite{krusty04www} analyzed requests from two video servers in the educational context. They show that the inter-request time
follows a Pareto distribution and the object popularity can be modeled by the concatenation of Zipf-like distributions. Differently, in our work, we present a
workload characterization of a user generated content video server. We are not aware of any other work that performs this type of characterization from the
point of view of the server.

Complementary to our effort, there are several works that characterize different aspects of online video sharing systems, especially YouTube.  In~\cite{moon2007}, the authors
analyze the distribution of popularity, evolution, and characteristics of YouTube videos, in addition to evaluate different approaches for the distribution of videos such as caches and
P2P sharing. Complementarily, Duarte \textit{et al.}~\cite{benevenuto@laweb07} characterizes geographical aspects of interactions of YouTube users. 
Rodrigues \textit{et al.}~\cite{rodrigues10@jbcos} studied differences on usage statistics and metadata of duplicated videos.
Gill \textit{et al.}~\cite{arlitt2007} present a workload characterization of YouTube from the point of view of a university, comparing its properties with the Web traffic and other video
servers. In~\cite{youtube_sessions}, the authors analyze the characteristics of user sessions on YouTube, by analyzing requests on a university proxy. However, the authors
evaluate only aspects such as the session duration and session creation, differently from us, who investigate different actions of users on a session. Zink \textit{et
al.}~\cite{cachelocal} perform simulations to show that video caching, on client or in a proxy, and P2P distribution can reduce network traffic and allow a fast access to video in
online video sharing systems. 

Recently, we presented a comprehensive characterization of the properties of the YouTube {\it video response network}, that is, the network that emerges from video-based user
interactions~\cite{benevenuto@tomccap09}.  In~\cite{benevenuto09@sigir}, we further characterize the behavior of three classes of users, namely, legitimate users, spammers and content promoters. Using a
machine learning algorithm and exploiting several attributes from the users' profiles, the users' social behavior in the system (i.e., the relationships established among them) and
from the user's videos we were able to detect the vast majority of the promoters and spammers. Finally, reference~\cite{benevenuto10@firstmonday} provides a comprehensive overview of
different sorts of malicious activities in video sharing systems as well as their implications for users and systems.

Differently from these efforts, our work here aims at not only characterizing and understanding the requests that arrive on user generated content video server, but also to investigate
and identify the profile of users who access these systems. Complementarily, Benevenuto~\textit{et al.}~\cite{benevenuto@imc09} used clickstream data to 
characterized user navigation and social interactions in online social networks, such as Orkut, Hi5, MySpace, and LinkedIn. 


