Characterizing SopCast client behavior

Borges, Ana Luíza Costa Cruz; Gomes, Pedro de Carvalho; Nacif, José Augusto M.; Mantini, Rodrigo; Almeida, Jussara M.; Campos, Sérgio Vale Aguiar

doi:10.1016/j.comcom.2012.02.012

Cited by 18 publications

(13 citation statements)

References 29 publications

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“…This behavior suggests that the use of client-side caching during sessions might be of limited benefit. Finally, some components of our client behavior model, notably session durations and data transfer times, present similarities, in terms of distribution models, with other multimedia and Web systems [17], [18]. However, the parameter values are very different.…”

Section: Summary and Future Workmentioning

confidence: 91%

“…Despite these differences, Figure 2 shows that the measured data in all three campuses is best-fitted by a Weibull distribution, with parameter values (see caption) depending on the dataset. This is a statistical distribution that has been used to model client active periods in other systems (e.g., Peer-to-Peer live streaming systems [17]). …”

Section: A Session Layermentioning

confidence: 99%

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Modeling the Dropbox client behavior

Gonçalves

Drago

Silva

et al. 2014

2014 IEEE International Conference on Communications (ICC)

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Cloud storage systems are currently very popular, generating a large amount of traffic. Indeed, many companies offer this kind of service, including worldwide providers such as Dropbox, Microsoft and Google. These companies, as well as new providers entering the market, could greatly benefit from knowing typical workload patterns that their services have to face in order to develop more cost-effective solutions. However, despite recent analyses of typical usage patterns and possible performance bottlenecks, no previous work investigated the underlying client processes that generate workload to the system. In this context, this paper proposes a hierarchical two-layer model for representing the Dropbox client behavior. We characterize the statistical parameters of the model using passive measurements gathered in 3 different network vantage points. Our contributions can be applied to support the design of realistic synthetic workloads, thus helping in the development and evaluation of new, well-performing personal cloud storage services.

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Section: Summary and Future Workmentioning

confidence: 91%

Section: A Session Layermentioning

confidence: 99%

Modeling the Dropbox client behavior

Gonçalves

Drago

Silva

et al. 2014

2014 IEEE International Conference on Communications (ICC)

Self Cite

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“…This is important as with that information a bad intentioned entity can direct attacks such as DDoS to those servers in order to harm the live streaming session. In [2,23] a characterization of traffic generated by SopCast is presented. One of the conclusions is that a malicious peer was able to compromise 50% of the network peers and 30% of the download bandwidth.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of Content Pollution in P2P Live Streaming Networks

Ziwich

Schimidt

Duarte

et al. 2013

2013 Sixth Latin-American Symposium on Dependable Computing

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Content pollution is one of the challenges for massively deploying live streaming P2P networks in the Internet. As the peers themselves are responsible to retransmit data, there is no trivial solution to this problem. This work presents a new strategy to detect content pollution that employs comparison-based diagnosis to identify modifications on the data stream. A peer compares randomly selected chunks received from its neighbors. Based on the comparison results, peers that transmitted polluted content are identified. The proposed solution was implemented using Fireflies, a scalable and intrusion-tolerant overlay network. Experimental results show that the strategy represents a feasible solution to detect content pollution and causes a low overhead in terms of network bandwidth.

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“…Despite the number of works characterizing P2P live streaming application [2][3][4][5][6], we are not aware of any public available dataset. Moreover, most of these works characterize a static view of the P2P systems, ignoring important dynamic aspects.…”

Section: Introductionmentioning

confidence: 99%

“…The datasets have been used in important works from our team. For instance, we have characterized the SopCast client behavior [2,8], producing models that researchers can use to test their own P2P live streaming systems. We discuss deeply our crawling methodology and present a brief SopCast characterization.…”

Section: Introductionmentioning

confidence: 99%

SopCast P2P live streaming

Vieira

Silva

Henrique

et al. 2013

Proceedings of the 4th ACM Multimedia Systems Conference

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P2P-TV applications have attracted a lot of attention from the research community in the last years. Such systems generate a large amount of data which impacts the network performance. As a natural consequence, characterizing these systems has become a very important task to develop better multimedia systems. However, crawling data from P2P live streaming systems is particularly challenging by the fact that most of these applications have private protocols. In this work, we present a set of logs from a very popular P2P live streaming application, the SopCast. We describe our crawling methodology, and present a brief SopCast characterization. We believe that our logs and the characterization can be used as a starting point to the development of new live streaming systems.

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Characterizing SopCast client behavior

Cited by 18 publications

References 29 publications

Modeling the Dropbox client behavior

Modeling the Dropbox client behavior

Diagnosis of Content Pollution in P2P Live Streaming Networks

SopCast P2P live streaming

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