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Characterization of P2P IPTV Traffic Scaling Analysis

Characterization of P2P IPTV Traffic Scaling Analysis
Characterization of P2P IPTV Traffic Scaling Analysis

a r X i v :0704.3228v 2 [c s .N I ] 17 J u l 2007

Technical Report v2

Characterization of P2P IPTV Tra?c:

Scaling Analysis

Thomas Silverston,Olivier Fourmaux and Kav′e Salamatian

Universit′e Pierre et Marie Curie -Paris VI Laboratoire LIP6/CNRS,UMR 7606

104avenue du pr′e sident Kennedy 75016Paris,France

Email:{thomas.silverston,olivier.fourmaux,kave.salamatian }@lip6.fr

Abstract

P2P IPTV applications arise on the Internet and will be massively used in the future.It is expected that P2P IPTV will contribute to increase the overall Internet tra?c.In this context,it is important to measure the impact of P2P IPTV on the networks and to characterize this tra?c.Dur-ing the 2006FIF A World Cup,we performed an extensive measurement campaign.We measured network tra?c generated by broadcasting soc-cer games by the most popular P2P IPTV applications,namely PPLive,PPStream,SOPCast and TVAnts.From the collected data,we charac-terized the P2P IPTV tra?c structure at di?erent time scales by using wavelet based transform method.To the best of our knowledge,this is the ?rst work,which presents a complete multiscale analysis of the P2P IPTV tra?c.

Our results show that the scaling properties of the TCP tra?c present periodic behavior whereas the UDP tra?c is stationary and lead to long-range depedency characteristics.For all the applications,the download tra?c has di?erent characteristics than the upload tra?c.The signaling tra?c has a signi?cant impact on the download tra?c but it has negligible impact on the upload.Both sides of the tra?c and its granularity has to be taken into account to design accurate P2P IPTV tra?c models.

1Introduction

P2P live streaming applications like P2P IPTV are emerging on the Internet and will be massively used in the future.The P2P tra?c counts already for a large part of the Internet tra?c and this is mainly due to P2P ?le-sharing applications as BitTorrent [1]or eDonkey [2].Video streaming services like Youtube [3]appeared only a few months ago but contribute already to an im-portant part of the Internet tra?c.It is expected that P2P IPTV will largely

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contribute to increase the overall Internet tra?c.It is therefore important to study P2P IPTV tra?c and to characterize its properties.

The characterization of P2P IPTV tra?c will allow us to understand its impact on the network.P2P IPTV applications have stringent QoS constraints (e.g.bandwidth,delay,jitter)and their tra?c characterization will enable to understand their exact needs in network resources.The knowledge of the tra?c properties enables the development of synthetic tra?c generation models that are key input parameters when modeling or simulating these systems.Indeed, the modeling or simulating steps are necessary to design judiciously applica-tions.From a tra?c engineering point of view,well understanding P2P IPTV tra?c is essential for Internet service providers to forecast their internal traf-?c and ensure a good provisioning of their network.And last but not least, global knowledge of the tra?c properties will highlight some drawbacks of the applications and will make it possible to improve the design of new P2P IPTV architectures.For instance,an important concern of these systems is the scal-ability.The tra?c characterization may help estimate the impact of overhead tra?c generated by the signaling.

In this paper,we present a multiscale analysis of the structure of the traf-?c generated by the most popular P2P IPTV applications,namely PPLive[4], PPStream[5],SOPCast[6]and TVAnts[7].

During the2006FIFA World Cup,we performed an extensive measurement campaign.We measured the network tra?c generated by broadcasting soccer games by the previously mentioned applications.The multiscale behavior of the collected tra?c is analyzed using a wavelet transform based tool.In this paper,we characterize the network tra?c of P2P IPTV systems at di?erent time scales and compare their properties.To the best of our knowledge,this is the?rst work that does a comparative multiscale characterization of P2P IPTV tra?c.

Our multiscale P2P IPTV tra?c analysis shows signi?cant di?erences in the scaling behaviors of TCP and UDP tra?c.the TCP tra?c presents periodic behavior while the UDP tra?c is stationary and presents long-range dependency characteristics,which will a?ect the quality of the video reception.The signaling tra?c has an impact on the download tra?c but it has negligible impact on the upload tra?c.The upload tra?c generated by P2P IPTV systems have di?erent scaling characteristics compare to the download tra?c and both sides of the tra?c has to be taken into account to design judiciously P2P IPTV tra?c models.Moreover,the tra?c granularity has to be considered while using tra?c models to simulate these systems.

The rest of the paper is organized as follows.Firstly,we present the related work in section2.In section3,we give an overview of the measured applications and describe our measurement experiment setup.In section4,we present our methodology to analyze the tra?c at di?erent time scales.We present our P2P IPTV tra?c analysis in section5and discuss the results in section6.Finally,

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we conclude the paper and give perspectives in section7.

2Related Work

Nowadays,an increasing number of P2P IPTV measurement studies is con-ducted to analyze the mechanisms of such systems.

Zhang et.al[8]present the?rst measurement results about their protocol Donet[9],which were deployed on the Internet and called Coolstreaming.They provide network statistics,like user’s behavior in the whole system and the qual-ity of video reception.

Hei et al.[10][11]made a complete measurement of the popular PPLive appli-cation.They made active measurements by instrumentalizing their own crawler and give many architecture and overlay details like bu?er size or number of peers in the networks.Vu et al.[12]made also active measurements of the PPLive system and derive mathematical models for the distributions of channel population size or session length.

In our previous work[13],we passively measured the network tra?c generated by several popular applications during a worldwide event.We compared the measured applications by inferring their underlying mechanisms and highlight their design di?erences and similarities.Ali et al.[14]made passive measure-ments of PPLive and SOPCast applications and analyze the performance and characteristics of such systems.

Still in their previously mentioned works,Ali et al.provide their own method-ology to study the data exchanges of such P2P applications.Based on their measurement studies,Hei et al.[15]developed also a methodology to estimate the overall perceived video quality throughout the network.

All these works studied P2P IPTV systems by measuring the tra?c and tried to infer their mechanisms,but they did not characterize the correlation structure of the generated tra?c at di?erent time scales to understand its properties and its impact on the network.

3Experiments

3.1P2P IPTV applications overview

For our P2P IPTV tra?c measurement experiments,we chose four applications, namely PPLive,PPStream,SOPCast and TVAnts because they were very pop-ular on the Internet.Whenever these applications are freely available,their source codes are not open and their exact implementation details and used pro-tocols are still widely unknown.We can only rely on reverse engineering to understand their transmission mechanisms.

All these applications claim to use swarming protocol like Donet.Similarly to BitTorrent,video data?ows are divided into data chunks and each peer down-loads the chunck of data to other peers concurrently.The peers know how to

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Figure1:Measurement experiments platform.Each node is a common PC directly connected to the Internet via campus network.

Table1:Packet traces summary

PPStream TVAnts

13,32112,198

Size(MB)4,1213,992

Download(%)20.5024.76

14.090.23

UDP010.05

Upload(%)79.5075.24

85.81 3.89

UDP013.57

download the video data chunks by exchanging randomly with other peers in-formation about the data chunks they have or neighbor peers they know.With this signaling tra?c,each peer discovers iteratively new peers,new available data chunks and is able to download video from several peers.In these P2P protocols,there are two kinds of tra?c:video tra?c where peers exchange data chunks with each other and signaling tra?c where peers exchange information to get the data.

As we show in[13],all the applications transport video and signaling tra?cs di?erently:PPStream uses exclusively TCP for all tra?cs while PPLive adds UDP for some signaling tra?c.SOPCast uses almost entirely UDP and TVAnts is more balanced between TCP(≈75%)and UDP for all kinds of tra?c.

In the next section,we will present the measurement experiments platform we used to collect the P2P IPTV tra?c.

3.2Measurement experiments platform

Our measurement experiments take place during the2006FIFA World Cup from09June to09July.We collected a huge amount of data,measuring most of the World Cup soccer games with di?erent applications at the same time and under di?erent network environments:campus Ethernet access and residential ADSL access.

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In this paper,we focus on four packet traces collected on June30in our cam-pus network:one for each measured application.In all our data,we selected these packet traces because they are well representative of all of them.The traces are made available on our traces sharing service[16].Two soccer games were scheduled,one in the afternoon(Germany vs.Argentine)measured by PPStream and SOPCast and one in the evening(Italy https://www.doczj.com/doc/b016818963.html,raine)measured by PPLive and TVAnts.We selected the four traces from di?erent applications to be able to characterize the P2P IPTV tra?c without being closely related to the design of the applications.

Our measurement experiment set up is described on Fig.1.To collect the packets,we used two personal computers with1.8GHz CPU and com-mon graphic card capabilities.The operating system running on the PCs was Windows XP.The PCs(nodes)were situated in our campus network and were directly connected to the Internet with100Mbps Ethernet access.During a game,each node was running a P2P IPTV application and we used tcpdump on each measuring node to collect all the packets.For all the measurement experi-ments,the consumed bandwidth was always relatively low and does not exceed 10Mbps.The Ethernet cards did not su?er any packet loss and captured all the packets.For all the experiments,nodes were watching CCTV5,a Chinese TV channel available for all the measured applications.It was important to watch the same TV channel with all the applications to assure that the behavior of users will be similar in each trace.For example,during the advertisement,what-ever the applications,an user may stop watching the channel and switches the application o?and then switch it on a few minutes later.All the applications used MPEG4video encoding.

Our platform has high-speed access and our observations can not be directly generalized to residential peers with common access to the Internet(e.g.20/1 Mbps or512/128Kbps).However,residential network capacities are quickly increasing and will have such high-speed access in only a few years when P2P IPTV would be commonly used.

Table1summarizes the four presented traces.At?rst,we can notice in these traces that PPLive,TVAnts and PPStream use massively TCP whereas only SOPCast uses mainly UDP.The duration of the traces is longer than the duration of a soccer game(≈105minutes).We chose to collect the tra?c a few minutes before and after the games to capture all the e?ects that the live interest of a soccer game could produce on the behavior of users(e.g.?ash crowds).

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P e r c e n t a g e o f P a c k e t s (%)

Packet size (Bytes)(a)PPLive

P e r c e n t a g e o f P a c k e t s (%)

Packet size (Bytes)

(b)SOPCast

P e r c e n t a g e o f P a c k e t s (%)

Packet size (Bytes)(c)PPStream

P e r c e n t a g e o f P a c k e t s (%)

Packet size (Bytes)

(d)TVAnts

Figure 2:Packet distributions for all the applications

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Analysis Methodology

4.1

Video and signaling tra?cs

The packet size distributions for all the applications are presented in Fig.2.PPLive has 75%of its packets larger than 1300Bytes and the other packets are very small (≈17%are 100Bytes length).Di?erently,SOPCast counts only about 30%of large packets (>1300Bytes)and 60%of its packets are very small (<100Bytes).PPStream counts more than 40%of packets bigger than 1400Bytes,almost 40%of its packets are 100Bytes and a small part is https://www.doczj.com/doc/b016818963.html,Ants packets seems more balanced in three equal parts:one part is 100Bytes,a second part is 1100Bytes and the third part is 1400Bytes.All the packets distributions of these applications are di?erent but we can distinguish two sets of packets within these packet distributions:small-size pack-ets (<200Bytes)and large-size packets (>1000Bytes).As we explained in section 3,the studied P2P applications generate two kinds of tra?c:video and signaling.

It is expected that the video tra?c is essentially composed of large-size packets.Most of the video packets should belong to the large-size packets set (>1000Bytes).The video data are also delay sensitive since video packets have to re-6

Table2:Signaling Tra?c Ratio

PPStream TVAnts

4.1%19.3%

Upload10.8%7.8%

19.2%48.5%

4.3Validation of the signaling heuristic

The measured applications are proprietary and we do not have any implemen-tation detail.We do not know exactly if a packet is for signaling tra?c or video tra?c.The heuristic used to?lter the signaling tra?c relies mainly on the packet size.The heuristic will perform well if it removes only signaling packets without removing any part of the video tra?c.

From Table2,we notice that the download signaling tra?c ratio computed by the heuristic for SOPCast is48.5%,which is a high ratio indicating that half of the tra?c would have been signaling tra?c.This high ratio of download signaling tra?c may eventually come from the inaccuracy of the heuristic.

To validate the?ltering heuristic,we compute the resulting video bitrate after removing the signaling tra?c.Realistic computed video bitrate will con-?rm the e?ciency of the heuristic.We compute the video bitrate for download tra?c because it is the only video tra?c we can deduce.The download tra?c is provided by other peers on the Internet and all the video?ows received by our controlled nodes compose the downloaded video.The video upload tra?c is not provided to a single consumer peer.We can not estimate the video bi-trate received by remote peers because they mix video?ows from many other providers peers to receive the entire video.In the following,we compute for all the traces the video download bitrate received by our controlled nodes by removing signaling tra?c with the presented heuristic.

Fig.2(b)indicates that SOPCast has more than60%of its packets smaller than100Bytes.These packet are certainly signaling packets according to their size.The design of SOPCast introduces a large amount of signaling packets and the heuristic removes these packets from the video tra?c.

Table1shows that the SOPCast download tra?c represents16.13%of the collected tra?c(5,475MBytes)and the download signaling ratio represents 48.5%of the overall download tra?c.The average bandwidth used by SOPCast to download the video at its bitrate can be computed by dividing the amount of received video data by the measurement experiment duration(12,198s).

(5475?220?8?0.1613)?(1?0.485)

Table3:Video Download Bitrate computed with the signaling?ltering heuristic

PPStream TVAnts

445305/360

For all the produced logscale diagrams,the X-axes are the octaves of the tra?c, which are the time scales of the packet arrivals.The right-most part of the graph is relative to large time scales and the left part is relative to small time scales.The Y-axes are the data energy spectra.A logscale diagram can be understood as follows:an octave j(X-axes)is a time scale of the packet tra?c energy spectrum.Since our bin is20ms,the octave j=8means the time scale t=28?20ms=5.12s.

LDEstimate is a tool that allow to visually observed the properties of the mea-sured tra?c.In a produced diagram,a bump in the energy spectrum indicates a possible periodic behavior of the tra?c,a constant energy spectrum a pos-sible memoryless process and a linear increase indicates a possible long-range dependency of the tra?c(LRD).

5Results Analysis

5.1Presentation

In the rest of the paper,we will refer to the tra?c trace of an application by the name of the application.For example,we will refer to SOPCast packet trace by SOPCast.

For each application,we study the tra?c by separating the upload tra?c and the download tra?c.In both tra?c directions,we separated the video tra?c from the overall tra?c by using the?ltering heuristic presented in section4.2.Then, each application is characterized by four distinct logscale diagrams:overall up-load tra?c,video upload tra?c,overall download tra?c and video download tra?c.Fig.3presents the logscale diagrams of the energy spectra for PPLive, Fig.4the energy spectra for SOPCast,Fig.5for PPStream and Fig.6for TVAnts.

Table1indicated previously that three of the measured applications use massively TCP(PPLive,PPStream and TVAnts)whereas only SOPCast uses mainly UDP.We will refer to an application using massively TCP as TCP ap-plication and UDP application for an application using UDP.

In the following,we will present tra?c di?erences between TCP and UDP applications in section5.2.In section5.3we will highlight the impact of the signaling tra?c for P2P IPTV applications.Then we discuss the stationnarity of the tra?c in section5.4.We will extend our?ndings in section5.5.The results are summarized and discussed in section6.

5.2TCP applications vs.UDP applications

For the TCP applications(PPLive,PPStream and TVAnts),the two upload energy spectra look similar for all the time scales(Fig.3(a)and3(b),Fig.5(a) and5(b),Fig.6(a)and6(b))The two download energy spectra look similar

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(a)Upload:overall packet tra?c(signaling and video tra?c)

(b)Upload:video packet tra?c

(c)Download:overall packet tra?c(signaling and video tra?c)

(d)Download:video packet tra?c

Figure3:PPlive packet tra?cs energy spectra.Bin duration is20ms.(Ex: Octave j=8is for scale process at t=28?20ms=5.12s).

until j=9and after they are di?erent(Fig.3(c)and3(d),Fig.5(c)and5(d), Fig.6(c)and6(d)).The upload energy spectra of TCP applications(3(a)and 3(b),5(a)and5(b),6(a)and6(b))do not look like their download energy spec-tra(3(c)and3(d),5(c)and5(d),(6(c)and6(d)).

All the TCP applications have similar energy spectra whatever the kind of tra?c and direction(e.g.overall or video).The observations for TCP applications may be generalized for all of those we measured(PPLive,PPStream and TVAnts). For UDP applications(SOPCast Fig.4),the four energy spectra look similar whatever the tra?c direction or the tra?c nature(e.g.overall or video tra?cs). The energy spectra of TCP applications(Fig.3,5and6)are di?erent from the energy spectra of UDP applications(Fig.4).

Regarding TCP applications energy spectra more precisely,we can observe an energy bump in all the logscale diagrams about time scale j=8(5.12s). The energy bump is more clearly de?ned in upload tra?c(Fig.3(a)and3(b),

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(a)Upload:overall packet tra?c(signaling and video tra?c)

(b)Upload:video packet tra?c

(c)Download:overall packet tra?c(signaling and video

tra?c)

(d)Download:video packet tra?c

Figure4:SOPCast packet tra?cs energy spectra.Bin duration is20ms.(Ex: Octave j=8is for scale process at t=28?20ms=5.12s).

Fig.5(a)and5(b),Fig.6(a)and6(b))than download tra?c(Fig.3(c)and3(d), Fig.5(c)and5(d),Fig.6(c)and6(d)).

The energy bump exists for all the applications using mostly TCP.The energy bump indicates a possible periodic behavior at these time scales whatever the tra?c direction or its nature.The energy bump phenomenon has to be con-?rmed by studying the stationnarity of the tra?c.We study tra?c stationnarity in section5.4.However,the stationnarity analysis shows that the energy bumps observed in the spectra are essential phenomena and not simply artifact coming from non-stationnarity.

The energy bumps are observed for all the TCP applications but not for the UDP applications.The well known TCP mechanisms used to transport data and TCP retransmission mechanisms could lead to such periodic tra?c behav-iors.However,the periodic behaviors are observed for time scale j=8(5.12s). A5seconds duration is a very long duration for TCP mechanisms.It is not so obvious that this periodic behavior is provided by TCP mechanisms.

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(a)Upload:overall packet tra?c(signaling and video tra?c)

(b)Upload:video packet tra?c

(c)Download:overall packet tra?c(signaling and video tra?c)

(d)Download:video packet tra?c

Figure5:PPStream packet tra?cs energy spectra.Bin duration is20ms.(Ex: Octave j=8is for scale process at t=28?20ms=5.12s).

The periodic behaviors could also come from the video broadcasted in the net-work.However,SOPCast does not show any energy bump in its energy spectra and SOPCast broadcasts also video in the network.

Currently,we can not surely establish the source of these periodic behaviors.It does not seem to come from TCP mechanisms nor broadcasted video.We are still investigating the periodic behavior we observed in the TCP applications energy spectra.

The energy bumps are characteristics shared by all the measured applications using massively TCP.They illustrate how the application design may impact the properties of the generated network tra?c.

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(a)Upload:overall packet tra?c (signaling and video tra?c)

(b)Upload:video packet

tra?c

(c)Download:overall packet tra?c (signaling

and video tra?c)

(d)Download:video packet tra?c

Figure 6:TVAnts packet tra?cs energy spectra.Bin duration is 20ms.(Ex:Octave j =8is for scale process at t =28?20ms =5.12s ).

5.3Impact of the signaling tra?c

For all the applications,whatever the transport protocol they use,their video upload energy spectra look like their overall upload energy spectra.This is illustrated on Fig.3(a)and 3(b),4(a)and 4(b),5(a)and 5(b),6(a)and 6(b).Removing the signaling tra?c has no impact on the upload tra?c.

Regarding the download tra?c,the video download energy spectra (Fig.3(d)4(d)5(d)and 6(d))are di?erent from their corresponding overall download en-ergy spectra (Fig.3(c)4(c)5(c)and 6(c)).Removing signaling tra?c from the download tra?c has clearly an impact on the download tra?c because it mod-i?es the download energy spectra.

Table 2shows the signaling tra?c ratio for all the applications in upload and download.For all the applications,the signaling tra?c represents a larger part in the download tra?c than the upload tra?c.

The upload signaling tra?c is only provided by our controlled node to other

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peers in the Internet.Since our node has high bandwidth capabilities,it serves video to many other peers.Table1indicates that the amount of upload tra?c is3to6times larger than the amount of download tra?c.The signaling tra?c sent by our node to other peers in the Internet counts only for a small part of the overall upload tra?c.

The download signaling tra?c is provided by other peers in the Internet to our controlled node.Our node just needs to download the video at the video bitrate. The download signaling tra?c coming from many other peers counts for a large part of the overall download tra?c.This explains the impact of signaling tra?c on the download tra?c.

To summarize our observations,the signaling tra?c has no impact on the upload tra?c but it has an impact on the download tra?c.In the following,we will discuss the stationnarity of the tra?c that will help to better characterize the properties of the P2P IPTV signaling tra?c.

5.4Tra?c stationnarity

From all the upload energy spectra(Fig3(a)3(b),Fig4(a)4(b),Fig5(a)5(b), and Fig6(a)6(b)),we observe a linear increase,starting from j=6for SOP-Cast,from j=9for PPLive,from j=12for PPStream or j=10for TVAnts. We also show that the linear increase exists in the download tra?c(Fig.3(c) 4(c)5(c)and6(c))but it is modi?ed(Fig.4(d))or wasted(Fig.3(d)5(d)6(d)) when removing signaling tra?c.

We already show that signaling tra?c has an impact on the download tra?c because it count for a larger amount of data.The signaling tra?c may also lead to the linear increase in the download energy spectra.

A linear increase indicates a possible long-range dependency of the tra?c (LRD).It means that the tra?c?uctuates largely and is not predictable.With such tra?c?uctuations,it becomes impossible to forecast the tra?c behavior or to make network provisioning.

The signaling tra?c is used by peers to get the video chunks they need.If the signaling tra?c is responsible for LRD,the signaling tra?c generates itself the troubles to download the video.In other words,the design of the appli-cations would be not e?cient if the signaling tra?c generates the long-range dependency of the tra?c.

For a P2P network,a LRD tra?c indicates,there is no stability in the network tra?c.Then,it becomes a hard task to provide QoS parameters(delay,band-width,jiiter)to users because networks conditions are always changing. Regarding P2P IPTV systems,which are delay sensitive,the tra?c LRD has to be avoided because it will directly a?ect and decrease the quality of video reception.For example,under a high churn of peers,each peer has always to discover new peers and to establish new partnerships with other peers to receive the video data chunks.In this case,there would be no stability in the overlay

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(a)Upload:overall packet tra?c(signaling and video tra?c)

(b)Upload:video packet tra?c

(c)Download:overall packet tra?c(signaling and video tra?c)

(d)Download:video packet tra?c

Figure7:TCP applications:PPLive.Tra?c stationarity for the three equal parts of the tra?c.The blue solid line is the?rst part,the dashed red line is the second part and the dash-dotted green line is the third part of the tra?c. network because of the disruptions in the peers connections and communica-tions.This would lead to non-predictable tra?c and long-range dependency of the tra?c.

In this section,we want to know if we really observe a long-range depen-dency of the tra?c.To this end,we study the stationnarity of the tra?c.We split each trace in three equal parts,and we used the previous wavelet transform method to analyze all the parts of the tra?c.We visually control the tra?c stability with LDEstimate.

Due to space limitations,we only present one example for TCP applications (PPLive,Fig.10)and one example for UDP application(SOPCast,Fig.11). The other TCP applications energy spectra are similar to the TCP example (Fig.10)and can be found in the appendix.

In each logscale diagram,we plot the three parts of the trace.The blue solid

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(a)Upload:overall packet tra?c(signaling and video tra?c)

(b)Upload:video packet tra?c

(c)Download:overall packet tra?c(signaling and video tra?c)

(d)Download:video packet tra?c

Figure8:UDP applications:SOPCast.Tra?c stationarity for the three equal parts of the tra?c.The blue solid line is the?rst part,the dashed red line is the second part and the dash-dotted green line is the third part of the tra?c. line is the?rst part of the trace,the dashed red line is the second part and the green dash-dotted line is the third part of the trace.The tra?c is stationary if each part of the tra?c looks like the other parts and has the same energy level.

For TCP applications(Fig.10),the three parts of the energy spectra are similar at small time scales but become di?erent at large time scales(about j=10).There is stationnarity in the tra?c until j=10.The tra?c is not stationary beyond j=10.

As shown in section5.2,the TCP applications experiment a bump in their en-ergy spectra at time scale j=8,whatever the tra?c direction or its nature. At this time scale,the tra?c is stationary and it demonstrates that the ob-served energy bumps are essential phenomenon and not simply artifact from non-stationnarity.On the contrary,the linear increase observed from j=9is not a long-range dependency of the tra?c.

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(a)PPLive Download:overall packet tra?c (signaling and video tra?c)

(b)PPLive Download:video packet tra?c

(c)SOPCast Download:overall packet tra?c (signaling and video

tra?c)

(d)SOPCast Download:video packet tra?c

Figure9:Top download?ows

For UDP applications(Fig.11),the three parts of the tra?c are similar and increase linearly.The tra?c of UDP applications is stationary.The linear increase characterizes a long-range dependency of the UDP applications tra?c represented by SOPCast.Removing signaling tra?c modi?es the linear increase in the SOPCast download tra?c.For UDP applications,signaling tra?c may lead the to the long-range dependency in the tra?c.

In this section,we show an important design di?erence between TCP and UDP applications.The tra?c of UDP applications is stationary and present long-range dependency whereas the tra?c of TCP applications is not stationary and does not experiments long-range dependency.

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Table4:Top download?ows

Overall tra?c Video tra?c

Volume(MB)Volume(MB)

PPLive82,42876,797

13.2111.79

SOPCast63,05447,187

53.8652.13

We notice for the two applications that their video energy spectra look sim-ilar to their overall energy spectra.This was expected because these?ows are sent by the top contributor peers and transport almost entirely video packets and not signaling packets.Removing signaling tra?c on these?ows can only have a limited impact,depending on the signaling packets in the?ows.

For example,the top download?ow of SOPCast transport15,867packets of signaling tra?c(63054?47187)counting for2.71MBytes whereas PPLive top download?ow transport only5,731signaling packets(82428?76797)counting for0.33MBytes.

Regarding TCP applications Fig.12(a)and12(b),until time scale j=10, the energy spectra of the top?ow look similar to the aggregate tra?c.Beyond this time scale,the energy spectra of the top download?ow are di?erent from the aggregate tra?c because the energy spectra of the top?ow are increasing. With UDP applications,until time scale j=11,the energy spectra of the top ?ow are di?erent from the aggregate tra?c.Fig.12(c)and12(d)show an energy bump at time scale j=3,then the energy spectra increase slightly from j=4 to j=11.Beyond j=11,we observe the linear increase usually observed for the UDP energy spectra.

In this experiment,we observe that the top?ows in the download tra?c do not have the same scaling properties as the aggregate download tra?c.We did the same experiments for the10th top download?ows(i.e.the10th?ow according to data volume transported).The10th top?ows present the same scaling properties as the top?ows.The plots for the10th top?ows can be shown in the appendix.

The aggregate tra?c is not only the mix of every single?ow.The granularity of the P2P IPTV tra?c has to be taken into account when designing P2P IPTV tra?c models.

6Results Discussion

In this work,we analyzed the P2P IPTV tra?c by using a wavelet based trans-form method.This allows us to characterize this tra?c and to understand its properties and impact on the network.Thanks to our original P2P IPTV tra?c analysis,we have many new?ndings and observations that have to be summa-rized and discussed.

First of all,we observed that the energy spectra of TCP applications are di?erent from the energy spectra of UDP applications(section5.2).One of the most relevant di?erence is the energy bump observed in the spectra of TCP applications at time scale j=8(5.12s),which indicates a possible periodic be-havior in the tra?c.Intuitively,we could believe these di?erences come from the two di?erent transport protocols used.However,a5seconds periodic be-haviors is a very long duration for TCP mechanisms and TCP should not be the

20

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iptv电视设置

a)连线 如果初次安装,请您不要担心连线问题,我们的现场工作人员会为您解决这个问题。如果您想自己了 解这方面的情况,这里将为您作简要介绍。 iTV机顶盒面板如下图所示: 连线方式有以下要点: ◆将<视频输出>和<音频左/右>与电视机的<视频输入>和<音频输入>相连。 ◆将WAN输入接口与网线相连。 具体连线方式如下图所示: b)设置

如果初次安装,请您不要担心设置问题,我们的现场工作人员会为您解决这个问题。如果出现故障,请您尽量打10000号联系我们,我们的工作人员将上门为您服务。 用户自己进行设置时,请您必须了解以下参数:iTV PPPOE帐号、iTV PPPOE密码、iTV 业务认证帐号、iTV 业务认证密码和域名服务器,如果没有这些参数,请拨打10000号与我们联系。 打开机顶盒电源后,连续按遥控按钮“#1”或“9721#”,电视机上将出现如下图所示的屏幕。 ·设置域名 温州iTV域名为https://www.doczj.com/doc/b016818963.html, 选择“域名设置”菜单后按遥控“确认”键,电视机上将出现如下图所示的屏幕: 按照下述方法输入域名: 按遥控器上的”切换”(P042C按F1)键,电视机屏幕下方将显示输入法,通过遥控“*”、“#”可以切换 不同的输入法和符号,如图:

连续按“#”键切换到“abc”小写字母输入模式,然后按遥控数字键“2”,按遥控左右键选中字符“b”,然后按“确认”键,所选字符就自动输入到“域名”文本框中。重复上述步骤,完成其他字母的输入。(这里以 域名“https://www.doczj.com/doc/b016818963.html,”为例说明) 然后按遥控“*”键切换到符号输入界面,左右键选择“.”,按“确认”键,完成输入“.”。 再按遥控“#”切换回小写字母输入模式,重复“步骤2”操作,完成完整域名的输入。 ※说明:按遥控器上的“返回”键可以删除文本框内的最后一个字符。 再次按遥控”切换”(P042C按F1)键,返回到正常模式。按遥控器上的方向键选中“确定”按钮,然后按遥控确认键,提交所输入的域名,返回到配置主页面。

IPTV网络电视解决方案

IPTV 网络电视解决方案 市场部提

一、IPTV网络电视系统IPTV网络电视系统的组成:

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赏的娱乐共享。开展了与以往的不同体验的IPTV,运营商将会受益于:吸引新的宽带用户,增加宽带接入放号 宽带用户贡献内容业务收入,提高宽带ARPU值 驱动宽带网络发展的规模化和智能化的良性成长 最终用户体验IPTV将会有全新的感受: 任君选择海量的电影和音乐歌曲,体会新颖的电视频道 在家或宾馆里与家人、朋友共享娱乐休闲 简单熟悉的遥控器操作 DVD的画面、Hi-Fi的音质效果 方案介绍 基于对IP电信网的深入理解,华为提供完整的IP TV宽带视频解决方案。具备终端业务感知能力,业务可控、可规划、可管理,完美实现免费业务和收费

IPTV无线解决方案及上网

我的e家IPTV无线解决方案 进线端

电视端

二、设备连接方式: iptv 口 lan 口 外线 ADSL MODEM 主无线路由器 副无线路由器 机顶盒 电视机 所需设备:1、ADSL MODEM (安装时电信赠送) 2、2台无线路由器(任何型号度汛,但必须有WDS 或bridge 桥接功能) 3、机顶盒(安装时电信赠送)

主要事项: 1、MODEM与主路由器必须用2根网线连接,一根连接MODEM的Internet出口(或网络1)和路由器的W AN口,另一根连接MODEM的IPTV出口 (或网络2)和路由器的任意一个LAN口。 2、副路由器的任意一个LAN口用网线与机顶盒的WAN口连接。 3、主副路由器要进行桥接设置(具体可以查找有关教程),关键步骤是2个路由器都要开启WDS或bridge桥接功能或选择AP(WDS+AP)工作模式,主 路由器桥接设置中MAC(或BSSID)输入副路由器的地址,副路由器的MAC(或BSSID)输入主路由器的地址。地址一般情况为:40-16-9F-52-5E-19也有个别华硕的路由器不用分隔符的如:40169F525E19。 4、主无线路由器要进行上网一般选择PPPoE方式,设置账号、密码就可以了,具体根据宽带接入方式不同而略有不同。

5、为了防止跟别人的无线路由器重名造成路由器不能桥接,应把自己的无线路由器明(或SSID号)设置的长一点,而且2个路由器的名字不能相同。 6、主路由器LAN的IP地址若为192.168.1.1,副路由器LAN的IP地址应与主路由器为同一网段不同地址,如:192.168.1.2,而且主路由器的DHCP服 务器要选择启用,副路由器的DHCP服务选择不启用。 7、设置完成一般情况就可以同时无线上网和无线看电视了,可以省掉那条从MODEM到机顶盒恼人的网线啦。

IPTV无线解决方案

IPTV 无线解决方案 一、简介。 由于目前大多数用户在装修时客厅电视机背后都没有预留网线接口,而modem 一般又放在书房,这样要使用IPTV 时,不得不布放一根明线来连接modem 和机顶盒。这样既不美观又不方便。如果有某种无法连接方式,那上面的问题就迎刃而解。经过我们实际测试,使用无线AP 加无线网桥的方式可以解决布明线的问题。 这种方式除传统的AP 外,还需要一台无线网桥,其作用是将无线信号与以太网电信号进行互相转换。连接方式视ADSL 或FTTB 不同接入方式而有些许差别,但总体原则是一致的:一,进线第一级设备(多端口modem 或小交换机)其internet 口与无线AP 的WAN 口连接,AP 设置PPPOE ,其下PC 机以有线或无线的方式通过AP 转发上网。二,第一级设备的IPTV 口与AP 的LAN 口连接,机顶盒通过反跳线与无线网桥的以太口连接;AP 与网桥以无线方式互联。 此无线方案优点在于:功能强大,可绑定天翼通。 缺点在于:对承重墙穿透能力弱。有一定的范围局限性。可以通过室内改线路,缩短直线距离可增强效果。 摆放方式:无线AP 与modem 放置在一起,而无线网桥和IPTV 机顶盒放置在一起。无线网桥的以太口用反跳线和IPTV 机顶盒的WAN 口连接。注意AP 和网桥之间以间隔一堵承重墙为宜。示意图如下: 另外无线局域网的三个重要指标是:无线路由器的发射功率、无线网卡或接收网桥的接收灵敏度(天线的大小)以及低速率连接(速率越低,性能越稳定,传输距离越远) 二、设备连接方式: iptv Room-A Room-B Room-C 无线网桥 无线AP Internet STB TV ADSL MODEM FE FE FE

软件测试习题集及答案(详细版)详解复习过程

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1开头错误代码主要为网络故障代码 1302 错误提示:连接服务器失败,请稍后再试一次。 故障原因:1、EPG主页地址没有通过有效性检测;2、针对EPG主页地址的域名解析失败;3、多次重试连接EPG主页地址失败,无法与其建立连接 处理方法:1.新开户的机顶盒请检查机顶盒的主认证服务器地址是否配置正确2.配置的地址为域名方式,可以通过Monitor工具连5030端口查看日志,确认是否域名解释失败3.检查机顶盒实际连接边缘EPG的MTU值的设置是否正确 1305 错误提示:非常抱歉,网络接入失败!请稍后再试一次,如果仍然失败,请拨打客户服务热线进行咨询故障原因: 故障原因:1、DHCP服务没有能够获得有效的IP地址;2、DHCP/DHCP+协议交互收到服务器返回的错误码 处理方法:检查机顶盒的DHCP相关配置参数是否正确,如鉴权、接入用户名和密码 1306 错误提示:设备异常,无法提供服务!请拨打客户服务热线进行咨询 故障原因:1、机顶盒没有进行初始化配置,没有任何配置信息;2、机顶盒的关键配置信息(比如MAC地址、EPG主页地址等)无效 处理方法:出现该故障,该机顶盒必须返修,需要通过串口或PC配置工具检测配置信息 1401 错误提示:非常抱歉,网络接入失败!请稍后再试一次,如果仍然失败,请拨打客户服务热线进行咨询 故障原因:ADSL拨号收到DSLAM/BAS的拒绝响应。 处理方法:机顶盒打开双栈时ADSL帐号或密码配置错误会提示1401,可进入系统设置界面修改,用户开通时,这些信息应该是已经配置正确的,如果使用过程中出现这个错误,应该只能向运营商咨询

1402 错误提示:非常抱歉,网络接入失败!请稍后再试一次,如果仍然失败,请拨打客户服务热线进行咨询 故障原因:ADSL拨号成功,但没有收到BAS服务器的响应 处理方法:网络原因与上层BAS链路异常,或者BAS服务异常 1403 错误提示:非常抱歉,宽带接入帐号或密码错误,网络接入失败!请稍后再试一次,如果仍然失败,请拨打客户服务热线进行咨询 故障原因:ADSL帐号或密码配置有误 处理方法:可进入系统设置界面修改,用户开通时,这些信息应该是已经配置正确的,如果使用过程中出现这个错误,应该只能向运营商咨询 1404 错误提示:非常抱歉,网络接入失败!请稍后再试一次,如果仍然失败,请拨打客户服务热线进行咨询 故障原因:ADSL拨号超时没有响应 处理方法:多次重试请检查上联的modem是否状态正常 1901 错误提示:非常抱歉,线路连接异常!请检查网线是否脱落或网络接入设备是否加电。检查后再试一次,如果仍然失败,请拨打客户服务热线进行咨询 故障原因:网线未插上 处理方法:确认网线是否接口松动 1902 错误提示:非常抱歉,无线网卡加载失败!请检查无线网卡是否连接正常,稍后再试一次,如果仍然失败,请拨打客户服务热线进行咨询 故障原因:用户选择无线接入模式,但没有检测到无线网卡

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