Statistical characterization of a real video on demand service: User behaviour and streaming-media workload analysis

https://doi.org/10.1016/j.simpat.2007.02.004Get rights and content

Abstract

The video on demand service characterized in this article has surpassed 4 years of service, and the access log database stores information of more than 160,000 reproductions and 900 videos. The wide variety of subjects, the range of content lengths and a frequent update of contents (several new videos every day), clearly differentiate this study from other previous research limited to specific users, subjects or environments. We present a statistical study of user behaviour and streaming traffic, analyzing session characteristics, mistaken reproductions, amount of media delivered, number and length of pauses and jumps in the reproduction, popularity and daily access profile. The results of the analysis will allow us to develop simulation models and workload generators to evaluate different scenarios and situations of the service.

Introduction

The speeds offered by the Internet access providers have been increasing continuously in recent years. As penetration grows, broadband providers in the OECD are increasingly offering voice and video services over their platforms [19]. The bandwidth enhancement in subscribers’ access capabilities has given rise to the appearance of Internet audio/video services, based mainly on streaming technology. Streaming media has been widely used over the Internet for an increasingly demanding and growing consumer population [35]. Thus, the characterization of streaming access workloads has become an essential factor to evaluate the performance of these services and their implications on the rest of the services in the network.

In this paper, a user behaviour analysis and workload characterization performed on the video on demand service of www.lne.es (La Nueva España Digital) is presented. This digital news service is one of the most successful in Spain and its video on demand service, called LNE TV, has interesting characteristics, such as a wide variety of subjects, a wide range of content lengths, a continuous content update (several new videos every day), etc. Over a period of 4 years the access log files have been captured and stored in a database to be analyzed. The obtained results have been compared with some previous classical studies, such as [2], [6], [12], [16], [33]. The results of the analysis have been used to develop user behaviour and workload traffic models which can be integrated in video on demand service models and load generators with the aim of evaluating future situations in the service. This paper makes the following contributions:

A primary contribution of our work is its statistical analysis of user behaviour in a commercial video on demand service over a 4-year period. The mistaken reproductions, amount of media delivered, number and length of pauses and jumps in the reproduction, session characteristics, popularity and daily access profile have been characterized. Unlike most previous media workload characterizations, which are focused on an educational content, the workload analyzed consists of accesses to a news site in Spain, containing variable-length and variable-subject entertainment content. Thus, the results improve and enrich current knowledge of typical media workload patterns.

Secondly, a streaming-media workload characterization has been performed, differentiating traffic per protocol, source device, audio/video flow and workload, during both regular load and buffering time intervals. The service uses RealNetworks technology.

Finally, we propose a simulation model of the audio/video on demand service. This model is oriented to services with different types of information and lengths.

The rest of the paper is organized as follows: in Section 2 other related works are analyzed. A general description of the case study is carried out in Section 3. The user behaviour analysis and characterization are presented in Section 4. A measurement study and characterization of RealMedia streaming traffic is carried out in Section 5. In Section 6 we propose a simulation model of the video on demand service. Finally, conclusions are presented in Section 7.

Section snippets

Related work

Video on demand analysis is a relatively recent field in the research world. Video on demand services are not widely deployed on the Internet, and, therefore, analysis studies are not abundant. In spite of that, some interesting papers on streaming service analysis have appeared during the last few years. These papers study different elements of user behaviour, quality of service and content popularity.

Elements such as session length, delivered time, user’s interactions, etc. have been studied

Case study

The presented study has been performed on the video on demand service of La Nueva España Digital (www.lne.es) which has an important number of accesses and has reached 8th position in the ranking of digital news sites in Spain. In 2001, www.lne.es presented its video on demand service (LNE TV) developed by the Computer Science Department of the University of Oviedo. The number of visits and the volume of information have risen since then.

User behaviour analysis and characterization

The analysis presented in this paper has been performed using the information obtained from the log files of the multimedia server. The final goal of the analysis is to construct a model of user behaviour. Thus, all the parameters which characterize this behaviour will be evaluated. A preliminary analysis has been presented in [32].

Users access the service during a session, which is composed by one or more reproductions with reflection periods (think time) between them (Fig. 1a). Each

Workload characterization

As the last stage of video on demand service characterization, this section analyzes the basic characteristics of the streaming-media workload. We differentiate the traffic per protocol, device, audio and video flow, regular load and buffering time.

Once the video is coded and stored, the media server transmits the data to the client through the network. Due to the effects of the network, the transmitted information reaches the receiver with a speed different to the playing rate of the client.

Client/server model for video on demand service

Once the behaviour of the different components of the video on demand service has been described, we developed a simulation model according to the functionality of the real system. Transitions and lengths of the states in the proposed model have been previously characterized statistically. We have designed a client model taking into account user behaviour and a server model considering the media workload described. All the models have been implemented using OPNET Modeler [20] as the simulation

Conclusions

In this paper, the server logs from a video on demand service have been analyzed in order to characterize the behaviour of users. In addition, the traces of RealAudio and RealVideo traffic captured from the system have allowed us to construct an accurate workload model. The collected information has permitted analysis that characterizes aspects such as the media data delivered, the number and length of the pauses, the popularity of the videos and many representative properties of one of these

Acknowledgements

This research has been financed by the network operator Telecable Asturias SAU and La Nueva España within the MediaXXI project and the Spanish National Research Program within the INTEGRAMEDIA project (TSI2004-00979).

References (36)

  • S. Acharya, B. Smith, P. Parnes, Characterizing user access to videos on the World Wide Web, in: Proceedings of MMCN,...
  • J.M. Almeida, J. Krueger, D.L. Eager, M.K. Vernon, Analysis of Educational Media Server Workloads, NOSSDAV. Port...
  • J.R. Arias, F.J. Suárez, D.F. García, X.G. Pañeda, V.G. García, Evaluation of Video Server Capacity with Regard to...
  • S. Boyden, A. Mahanti, C. Williamson, Characterizing the Behaviour of RealVideo Streams, in: Proceedings of SCS SPECTS...
  • L. Cherkasova et al.

    Analysis of enterprise media server workload: access patterns, locality, content evolution and rates of change

    IEEE/ACM Transactions on Networking

    (2004)
  • M. Chesire, A. Wolman, G. Voelker, H. Lavy, Measurement and Analysis of a Streaming-Media Workload, in: USENIX...
  • J. Chung, M. Claypool, Y. Zhu, Measurement of the Congestion Responsiveness of RealPlayer Streaming Video Over UDP, in:...
  • J. Chung et al.

    Empirical evaluation of the congestion responsiveness of RealPlayer video streams

    Multimedia Tools and Applications

    (2006)
  • C. Costa, I. Cunha, A. Borges, C. Ramos, M. Rocha, J. Almeida, B.Ribeiro-Neto, Analyzing Client Interactive Behavior in...
  • C. Costa, I. Cunha, C. Ramos, J. Almeida, GENIUS: Generator of Interactive User media Sessions, in: Proceedings IEEE...
  • R. García et al.

    Analysis and Modelling of a Broadband Fiber Access Network with High Peer-to-Peer Traffic Load

    (2006)
  • C. Griwodz et al.

    Long-term Movie Popularity in Video on Demand System

    (1997)
  • S. Jin, A. Bestavros, GISMO, A Generator of Internet Streaming Objects and Workloads, ACM SIGMETRICTS...
  • T. Kuang, C. Williamson, A Measurement Study of RealMedia Audio/Video Streaming Traffic, in: Proceedings of SPIE ITCOM,...
  • A.M. Law et al.

    Simulation Modelling and Analysis

    (2000)
  • D. Loguinov, H. Radha, Measurement Study of Low-bit rate Internet Video Streaming, ACM SIGCOMM Internet Measurement...
  • A. Mena, J. Heidemann, An Empirical Study of Real Audio Traffic, in: Proceedings of the IEEE Infocom, Tel-Aviv, Israel,...
  • J. Nichols, M. Claypool, R. Kinicki, M. Li, Measurements of the Congestion Responsiveness of Windows Streaming Media,...
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