4,687 research outputs found
P2P IPTV Measurement: A Comparison Study
With the success of P2P file sharing, new emerging P2P applications arise on
the Internet for streaming content like voice (VoIP) or live video (IPTV).
Nowadays, there are lots of works measuring P2P file sharing or P2P telephony
systems, but there is still no comprehensive study about P2P IPTV, whereas it
should be massively used in the future. During the last FIFA world cup, we
measured network traffic generated by P2P IPTV applications like PPlive,
PPstream, TVants and Sopcast. In this paper we analyze some of our results
during the same games for the applications. We focus on traffic statistics and
churn of peers within these P2P networks. Our objectives are threefold: we
point out the traffic generated to understand the impact they will have on the
network, we try to infer the mechanisms of such applications and highlight
differences, and we give some insights about the users' behavior.Comment: 10 page
An experimental study of client-side Spotify peering behaviour
Spotify is a popular music-streaming service which has seen widespread use across Europe. While Spotify’s server-side behaviour has previously been studied, little is known about the client-side behaviour. In this paper, we describe an experimental study where we collect packet headers for Spotify traffic over multiple 24-hour time frames at a client host. Two distinct types of behaviour are observed, when tracks are being downloaded, and when the client is only serving requests from other peers. We also note wide variation in connection lifetimes, as seen in other studies of peer-to-peer systems. These findings are relevant for improving Spotify itself, and for the designers of other hybrid peer-to-peer and server-based distribution architectures
Hypersparse Neural Network Analysis of Large-Scale Internet Traffic
The Internet is transforming our society, necessitating a quantitative
understanding of Internet traffic. Our team collects and curates the largest
publicly available Internet traffic data containing 50 billion packets.
Utilizing a novel hypersparse neural network analysis of "video" streams of
this traffic using 10,000 processors in the MIT SuperCloud reveals a new
phenomena: the importance of otherwise unseen leaf nodes and isolated links in
Internet traffic. Our neural network approach further shows that a
two-parameter modified Zipf-Mandelbrot distribution accurately describes a wide
variety of source/destination statistics on moving sample windows ranging from
100,000 to 100,000,000 packets over collections that span years and continents.
The inferred model parameters distinguish different network streams and the
model leaf parameter strongly correlates with the fraction of the traffic in
different underlying network topologies. The hypersparse neural network
pipeline is highly adaptable and different network statistics and training
models can be incorporated with simple changes to the image filter functions.Comment: 11 pages, 10 figures, 3 tables, 60 citations; to appear in IEEE High
Performance Extreme Computing (HPEC) 201
Passive characterization of sopcast usage in residential ISPs
Abstract—In this paper we present an extensive analysis of traffic generated by SopCast users and collected from operative networks of three national ISPs in Europe. After more than a year of continuous monitoring, we present results about the popularity of SopCast which is the largely preferred application in the studied networks. We focus on analysis of (i) application and bandwidth usage at different time scales, (ii) peer lifetime, arrival and departure processes, (iii) peer localization in the world. Results provide useful insights into users ’ behavior, including their attitude towards P2P-TV application usage and the conse-quent generated load on the network, that is quite variable based on the access technology and geographical location. Our findings are interesting to Researchers interested in the investigation of users ’ attitude towards P2P-TV services, to foresee new trends in the future usage of the Internet, and to augment the design of their application. I
A Resource Intensive Traffic-Aware Scheme for Cluster-based Energy Conservation in Wireless Devices
Wireless traffic that is destined for a certain device in a network, can be
exploited in order to minimize the availability and delay trade-offs, and
mitigate the Energy consumption. The Energy Conservation (EC) mechanism can be
node-centric by considering the traversed nodal traffic in order to prolong the
network lifetime. This work describes a quantitative traffic-based approach
where a clustered Sleep-Proxy mechanism takes place in order to enable each
node to sleep according to the time duration of the active traffic that each
node expects and experiences. Sleep-proxies within the clusters are created
according to pairwise active-time comparison, where each node expects during
the active periods, a requested traffic. For resource availability and recovery
purposes, the caching mechanism takes place in case where the node for which
the traffic is destined is not available. The proposed scheme uses Role-based
nodes which are assigned to manipulate the traffic in a cluster, through the
time-oriented backward difference traffic evaluation scheme. Simulation study
is carried out for the proposed backward estimation scheme and the
effectiveness of the end-to-end EC mechanism taking into account a number of
metrics and measures for the effects while incrementing the sleep time duration
under the proposed framework. Comparative simulation results show that the
proposed scheme could be applied to infrastructure-less systems, providing
energy-efficient resource exchange with significant minimization in the power
consumption of each device.Comment: 6 pages, 8 figures, To appear in the proceedings of IEEE 14th
International Conference on High Performance Computing and Communications
(HPCC-2012) of the Third International Workshop on Wireless Networks and
Multimedia (WNM-2012), 25-27 June 2012, Liverpool, U
Analyzing Peer Selection Policies for BitTorrent Multimedia On-Demand Streaming Systems in Internet
The adaptation of the BitTorrent protocol to multimedia on-demand streaming
systems essentially lies on the modification of its two core algorithms, namely
the piece and the peer selection policies, respectively. Much more attention
has though been given to the piece selection policy. Within this context, this
article proposes three novel peer selection policies for the design of
BitTorrent-like protocols targeted at that type of systems: Select Balanced
Neighbour Policy (SBNP), Select Regular Neighbour Policy (SRNP), and Select
Optimistic Neighbour Policy (SONP). These proposals are validated through a
competitive analysis based on simulations which encompass a variety of
multimedia scenarios, defined in function of important characterization
parameters such as content type, content size, and client interactivity
profile. Service time, number of clients served and efficiency retrieving
coefficient are the performance metrics assessed in the analysis. The final
results mainly show that the novel proposals constitute scalable solutions that
may be considered for real project designs. Lastly, future work is included in
the conclusion of this paper.Comment: 19 PAGE
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