On Characterizing PPStream: Measurement and Analysis of P2P IPTV under Large-Scale Broadcasting

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1 On Characterizing PPStream: Measurement and Analysis of P2P IPTV under Large-Scale Broadcasting Wei Liang, Jingping Bi, Rong Wu, Zhenyu Li Institute of Computing Technology, Chinese Academy of Sciences, Beijing 9, P.R. China Chen Li Beijing University of Posts and Telecommunications, Beijing 876, P.R. China Abstract Despite the recent and remarkable popularity of P2P IPTV systems, the study of their characteristics on largescale broadcasting is rare. In this paper, we have undertaken a measurement study on one of the most popular P2P IPTV systems, PPStream, during the 29th Olympics broadcasting. We deployed our dedicated measurement tool, PPS-Sniffer, under different network environments, and collected extensive data during a large number of events. We evaluate the playback delay, and the peering strategies towards local cluster. We also investigate the data scheduling, and its impact on the playback quality and the control overhead. To the best of our knowledge, results obtained in our study are unique and new as compared with existing work. Specifically, our study demonstrates that:.) The playback delay is notable in Olympic channels, even for close peers. 2.) Peering strategies of PPStream target mainly on the ISP level, and should exploit peers of local clusters to reduce the playback lag among close peers. 3.) We unveil an overhead-quality tradeoff that sheds light on the design of future systems that can achieve a good balance. Our study aids the understanding of significant performance and design issues of PPStream, and is also useful for optimizing, modeling and the future design of P2P IPTV systems. Index Terms Measurement, Peer-to-Peer Streaming, IPTV. I. INTRODUCTION Peer-to-peer (P2P) technologies have been applied to a wide range of applications, in particular, file sharing and VoIP. Recently, with the successful deployments of many P2P IPTV (P2P streaming) systems, such as PPLive [], PPStream [2], UUSee [3], CoolStreaming [4], and etc., P2P has proved to be an efficient solution to deliver multimedia to millions of simultaneous users across the Internet [5]. There are primarily two factors behind this: First, the P2P technology requires minimum support from the infrastructure. Second, the participating peers contribute their upload bandwidth capacities to serve one another, thus the load on streaming servers is significantly mitigated. The research community has given great attention to P2P IPTV systems in the recent two years. However, understanding the characteristics of these systems under large-scale broadcasting is rare. Existing measurement studies on P2P IPTV systems can be classified into two categories, depending on whether one has the direct control of the system. If one does so, the measurement analysis can be conducted based upon various system logs. In [4], which is the founder of the P2P mesh-pull live streaming system, performance evaluation of CoolStreaming was made over PlanetLab, and the system logs showed that mesh-pull streaming systems achieved more continuous media playback than tree-push streaming systems. Their subsequent work in [6] examined the workload characteristics, system dynamics and performance impact from a variety of system parameters. Utilizing the system logs provided by UUSee, Wu et. al. characterized the topology properties of UUSee overlay in [7, 8] and the UUSee streaming traffic patterns in [9]. On the other hand, if the systems are not directly accessible, general measurement techniques, either passive sniffing or active crawling, will be adopted. Hei s work [] is the first of such measurement studies. Subsequently, they further investigated the traffic patterns and peer dynamics of PPLive in [] and studied the network-wide quality, including video playback continuity, initial startup latency and playback lags in [2]. By defining a novel experiment setup, Michela Meo et. al. measured P2P IPTV systems under adverse network conditions [3]. Their results showed that while the behavior of P2P IPTV is effective in trying to overcome network impairment, it poses a problem when considering the network and application friendliness. Authors in [4] measured PPStream and gave some preliminary descriptions on geographic clustering, connection stability, and topology. In [5] and [6], authors studied the traffic characteristics by comparing different P2P IPTV systems. The focus in this paper is different from all prior work in that we measure the performance and characteristics of PPStream under large-scale broadcasting, the 29th Olympic broadcasting, and make comparisons with existing studies and also with the ordinary channel of PPStream. PPStream is one of the most popular P2P IPTV systems in Asia and across the world. It provided four Olympic channels, CCTV-, CCTV-2, CCTV-5 and CCTV-7, to live broadcast the Olympic sports events. After decoding the proprietary protocol of this commercial system, we developed our dedicated measurement tool PPS-Sniffer, and deployed it in five controlled nodes under two typical network environments: campus and residential

2 networks. The measurement campaign was carried out during the period of the 29th Olympics with our controlled nodes watching a large number of events at the same time. Thanks to this large-scale and live-interest broadcasting event, we obtained a representative sample of P2P live streaming. Based upon the extensive data traces we have collected, we first evaluate the playback delay. Results show that the playback delay is more remarkable in Olympic channels, even for close peers. We then explore the peering strategies of PPStream towards local cluster. We demonstrate that peering strategies of PPStream target mainly on the ISP level. Nevertheless, finer granularity in peer selection algorithm should exploit peers of local clusters in order to reduce the large lag effect among end users nearby. Finally, we investigate the data scheduling in Olympic channels, and make comparisons with that in an ordinary channel. We demonstrate that better playback quality is achieved in Olympic channels at the cost of increased control overhead, as compared with the ordinary channel. Therefore, we unveil an overhead-quality tradeoff that sheds lights on the design of future systems. To the best of our knowledge, insights gained in our study are unique and new as compared with existing work. Our study aids the understanding of significant performance and design issues of PPStream, and is also useful for optimizing, modeling and the future design of P2P IPTV systems. The rest of the paper is organized as follows. In Section II, we provide an overview of mechanisms of P2P IPTV systems and the proprietary protocol details of PPStream. Our measurement settings are described in Section III. We then present and discuss the measurement results in Section IV. Finally, Section V offers the conclusion of the paper and our future work. II. PROTOCOL DECODING Through numerous experiments and protocol analyses, we gain a better understanding of the underlying mechanisms employed by mesh-pull P2P IPTV systems [5]. The whole procedure of how a P2P streaming peer joins the system is described as follows. When an end-user launches the software client, it connects to the channel list server to update the channel list. Once an end user selects a channel, the peer node joins the corresponding overlay of the selected channel. It first requests the peer list servers to retrieve an initial list of peers that are in the same overlay. The peer node then communicates with the peers in the initial list to obtain additional peers in a gossip manner. Peers are identified by their IP addresses and port numbers, and they are returned in messages which we call peer-list messages. After aggregating enough peers, the peer node attempts to connect to these peers to download video data, which are divided into chunks. To this end, peers send to each other buffer-map messages, which denote what chunks a peer has currently buffered and are available for sharing. Based on these messages, the peer node is able to decide where to download the required chunks. Then it sends out data-request messages to request the specific chunk from its peers. After accumulating enough chunks of video data in the buffer for playing, the media player pops up and the program begins to play. Before delving into the characteristic study of PPStream, we provide a brief introduction to its proprietary protocol details. Opposite to previous measurement results in [6], PPStream used UDP almost exclusively to transport video and signaling traffics in the four Olympic channels. Peers are presented by their IP addresses and UDP port numbers in the peerlist messages. Each video chunk is identified by the actual playback time, which we call chunk-id and is encoded by the program source. Each chunk is divided into 28 sub-chunks. Each sub-chunk is further divided into 8 small-chunks with size of KB for each. Hence a chunk contains video streaming of about 2 seconds for the 39kbps Olympic channels. Each buffer-map message includes a chunk-id and a 28-bit string of zeros and ones indicating which sub-chunks are available. Each data-request message contains a requesting chunk-id and a small-chunk sequence number which can be converted to the corresponding sub-chunk in this chunk. III. MEASUREMENT SETTINGS Based on the knowledge of how P2P IPTV systems generally operate and our understanding on the proprietary protocol details of PPStream, we developed our dedicated measurement tool PPS-Sniffer, which runs on the Microsoft Windows platform with easy installation because PPStream is implemented under this OS. PPS-Sniffer passively captures three types of PPStream control traffic as mentioned in Section II: peer-list messages, buffer-map messages and data-request messages. Since only the most important control traffic is captured, the computation complexity of the data analysis and the storage space for data traces are substantially reduced. Therefore, we believe our measurement tool is more suitable for long-term monitoring, as compared with existing passive measurement techniques. As most of the PPStream users today have two types of network connections, we deployed our PPS-Sniffer in five controlled nodes: three were connected to the campus network with Mbps Ethernet access; and the other two were connected to residential networks through ADSL access. The five nodes were all running NTP [7] to assure time synchronization. Our measurement started from the opening ceremony of the 29th Olympic Games on August 8th, 28 to the closing ceremony on August 24th, 28. We collected 5.8GB of control traffic for 52 sports events in total. We have made all of the collected data downloadable, and a more detailed description on the data format and the dataset can be accessed at ftp://ppsdata:ppsdata@ We limit the scope of our analysis to several typical sports events that are representative of our data set. Table I provides an overview of our measurement settings. Four of our controlled nodes were watching the same sports events in Olympic channels, CCTV, CCTV2, CCTV5 or CCTV7, at the same time. To make a comparison, meanwhile, we had the other one node, CampusC, watching an ordinary channel of PPStream, namely, First Finance.

3 TABLE I MEASUREMENT SETTINGS Name Bandwidth Channel Control traffic(bytes) CampusA LAN Olympic channel 4.2G CampusB LAN Olympic channel 3.8G CampusC LAN ordinary channel 3.G ResidenceA ADSL Olympic channel 3.6G ResidenceB ADSL Olympic channel 4.2G PDT (sec) Campus A Campus B Residence A Residence B A. Playback Delay IV. MEASUREMENT AND ANALYSIS Due to the best-effort nature of the underlying Internet and the deployment of the buffering mechanisms in P2P streaming systems, the progress of video playback on some peers may be minutes behind other peers and even longer behind that of the program source. We refer to this phenomenon as playback delay, which affects the viewing experiences of end users. While a certain amount of playback delay is inevitable, shorter playback delay is desired. Recall that each buffer-map message includes a chunk-id, which is the actual playback time encoded by the program source. We therefore define the playback delay time (PDT) of a peer as the time interval between the advertising time of the buffer-map message and the actual playback time, which is represented by the chunk-id in this buffer-map message. Since the advertising process of the buffer-map message as well as the chunk-id in it increases with the playback, it is reasonable to use PDT as an approximate reference to the playback delay between peers and the source and also among peers in the same channel. Figure plots the evolution of the PDT of our four controlled nodes during two hours of the opening ceremony in CCTV5 on August 8th. We can see that the playback delay effect was notable - CampusA was about 2 seconds behind the program source and about 9 seconds behind ResidenceB, and there was even about 8 seconds delay between CampusA and CampusB, which were in the same LAN. As measured by previous study in [4], the average playback delay of PPStream is less than 2 seconds and the number of unique users per channel is about 6, on average everyday. However, the delay effect was more remarkable in this large-scale broadcast event, and there were approximately 7,, users watching the opening ceremony in CCTV5 simultaneously as shown by PPStream. The notable delay is largely due to the extremely large number of simultaneous peers. This is because the overlay radius, which is measured by the number of overlay hops, reflects the media-delivering latency to a certain extent. Moreover, the overlay radius increases with the overlay size, because it is bounded by O(log N), as modeled in [4], where N is the overlay size. We now focus on the delay differences between CampusA and CampusB, which were in the same LAN, since a large playback lag among close peers will compromise the viewing experiences, especially in live broadcasting. It is interesting CDF Fig.. Time (sec) The playback delay differences Fig. 2. PartnerOverlap (%) The partner overlap of CampusA and CampusB to examine whether and to what extent the overlay topology of PPStream includes peers of local cluster to exchange data. Towards this end, we define the partner overlap of two nodes during a same time period. Let P(x) denote the partners of node x, which is the set of peers that exchange video data with node x, and can be extracted from the collected data-request messages. Let P(x) denote the cardinality of P(x), then the partner overlap of node A and B is defined as follows. P (A) P (B) P arentoverlap(a, B) = () P (A) P (B) We observe, interestingly, that the partner overlap of CampusA and CampusB is zero during the time period as in Figure, although there were 3,5 and 3,3 peers exchanging video data with CampusA and CampusB, respectively. Examination of other traces of these two nodes is plotted in Figure 2. We can see that for approximate 7% of the cases, the partners overlap is less than 2%, and it is more than 8% for about only

4 Proportion Fig. 3. ChinaNetwork ChinaTelecom ChinaRailway ChinaMobile ChinaUnicom CSTNET Others CampusA CampusB ResidenceA The ISP distribution of partners of CampusA and CampusB % of the cases. It seems that the two nodes would like to select partners independently and the local peers are not aware of each other. This suggests that the peer selection mechanism of PPStream does not exploit peers of local clusters. According to the results in Figure 2, we assess whether PPStream exploits locality information while selecting partners. Therefore, we further explore the structure of the P(x) for our controlled nodes, particularly the ISP distribution. By querying the IPtoASN database from Team Cymru [8], Figure 3 reports the ISP distribution of the P(x) of CampusA and CampusB, which reside in CSTNET. For the purpose of comparison, Figure 3 also reports the ISP distribution of the P(x) of ResidenceA which is in ChinaNetcom during the same period. It is shown that, indeed, partners from ChinaNetcom and ChinaTelecom are in the majority for all three controlled nodes, given that they are the largest ISPs in China, whereas CSTNET is much smaller. However, we can still observe the differences between the composition of partners of ResidenceA and that of CampusA or CampusB. Specifically, over 75% of partners of ResidenceA are from the same ISP in which it resides, and few partners are from CSTNET. In comparison, certain percentage of partners is from CSTNET for both CampusA and CampusB. Similar results can be obtained with other traces. In addition, we observe that several partners were hosts in North America. Since the rights of PPStream to broadcast the 29th Olympics were restricted to China by the commercial contract with CCTV.com [9], we cannot observe whether this exception is due to the inaccuracy of the database from Team Cymru or, instead, to the inefficiency of PPStream s filtering mechanisms. The results in Figure 2 and Figure 3 allow us to conclude that the peering strategies of PPStream target mainly on the ISP level, probably for the purpose of traffic engineering, e.g., controlling and reducing inter-isp traffic. Nevertheless, finer granularity in the peer selection algorithm, which exploits peers of local cluster to exchange data, will likely mitigate the large lag effect among close peers. B. Data Scheduling Given the buffering information of the partners of a node, a schedule is to be generated for fetching the expected video data from the partners, which we call parents. This is what we mean by data scheduling, which is important to guarantying the continuity of the playback and which is the key of efficient system design. In this subsection, we analyze the data scheduling of PPStream in Olympic channels. In accordance with previous measurement in [4], each data chunk, in ordinary channels of PPStream, is divided into 28 sub-chunks with size of 8KB for each. This subchunk is the basic unit for data scheduling, which is known as the scheduling unit. Deviating from this, we find that each sub-chunk, in Olympic channels, is further divided into 8 smaller units of KB for each. We refer to this smaller unit as small-chunk. It is the scheduling unit in Olympic channels as observed in data-request messages described in Section II. It seems that the effect of this change of design option of PPStream in Olympic channels is to increase the number of potential suppliers of a scheduling unit. Because it becomes easier for a node to find an appropriate parent for downloading data if a smaller scheduling unit is used, the utilization of small-chunk promotes the reliable delivery of the video data, and thus the upgrade of the playback quality. To validate and evaluate this claim, we investigate the following two metrics: ) parent number per sub-chunk: We first extract the parent of each small-chunk from data-request messages, and then count the number of different parents of the 8 small-chunks in a sub-chunk. This term indicates that, to obtain the same amount of data, utilizing smaller scheduling units in Olympic channels explores more potential suppliers, as compared with ordinary channels. 2) data miss ratio: It is defined as the number of sub-chunks that are not represented in the buffer-map messages over the total number of sub-chunks. The data chunks, which are not represented in the buffer-map messages, are probably missing the playback deadlines and are skipped during the playback. Therefore, this term directly reflects the playback quality at the user end. In Figure 4, we plot the distribution of the parent number per sub-chunk of CampusA during a football game in CCTV5 on August 3th, which held totally 27,367 sub-chunks. It shows that 78% of these sub-chunks have more than one parent. Five and even six small-chunks in a sub-chunk are provided by different parents for about 9% of the total cases. In other words, to obtain a sub-chunk, the Olympic channel indeed increases the number of suppliers, given that there is only one supplier for a sub-chunk in the ordinary channel. Figure 5 shows the cumulative distribution of the data miss ratio in three traces: the opening ceremony in CCTV5 in CampusA (trace), the men s mountain bike game on August 2th in CCTV in CampusA (trace2) and the ordinary channel in CampusC (trace3). Of the three traces, the average user numbers in trace2 and trace3 are of the same order of magnitude, which are 33,276 and 33,38, respectively, as

5 2 Parents 28% Parent 22% Others < % Proportion 6 Parents 7% 2% 5 Parents The number of requests per small trunk (A) 3 Parents 26% 4 Parents 6% Proportion.2 Fig. 4. The distribution of the number of parents per sub-chunk The number of requests per sub trunk (B).9 Fig. 6. The distribution of the number of requests per scheduling unit CDF Fig. 5. trace. trace2 trace Miss Ratio (%) The cumulative distribution of data miss ratio in three traces we physically recorded the online user number shown by PPStream during the course. We can see in Figure 5 that, for the two Olympic channels, the data miss ratio is zero for about 94% and 88% of all data. The average data miss ratio is 3% and 6%, respectively. In comparison, the data miss ratio in the ordinary channel has a wide variability. It is larger than % for about 28% of all data, with the average of.3%. The results confirm that by increasing the number of potential suppliers, smaller scheduling units help in reducing the data miss ratio and in upgrading the playback quality. However, the better playback quality in the Olympic channels comes at the cost of high control overhead. Without loss of generality, we first assume that only one data request is required to obtain each scheduling unit. Obviously, to obtain a sub-chunk, only one data request is necessary in the ordinary channel. Whereas, 8 data requests are required in the Olympic channel because each sub-chunk is further divided into 8 smaller scheduling units in the Olympic channel. To relax the above assumption, we then examine the scheduling performance of the Olympic channel, and make a comparison with that of the ordinary channel. To address this problem, we investigate the number of requests per scheduling unit and its mathematical expectation. The mathematical expectation is the average value of the number of data-request messages sent by a peer for fetching each scheduling unit, which is small-chunk in the Olympic channel or sub-chunk in the ordinary channel. Therefore, the number of requests per scheduling unit and its mathematical expectation represent the efficiency of the data scheduling, and reflect the control overhead. Figure 6(A) shows the distribution of the number of requests per scheduling unit for CampusA watching CCTV5, while Figure 6(B) is for CampusC watching the ordinary channel. The dataset for each channel contains equally 28,986 subchunks, which are 23,888 small-chunks for the Olympic channel. We can see that, in the Olympic channel, one datarequest is required for up to 64% of the total scheduling units. Whereas, two or more data-requests remain for over 36% of the small-chunks, in particular, about 26% of which need two data-requests and more than 7% of which need three data-requests. For the ordinary channel, on the other hand, only one data-request is required for almost all (nearly 97%) of the scheduling units, whereas, merely 3% of subchunks require more than one data-request. The mathematical expectation of the number of requests per scheduling unit is.48 and.3, respectively, for the Olympic channel and the ordinary channel. Considering that PPStream heavily relies upon TCP as transport protocol in ordinary channels, while using exclusive UDP to transport video and signaling traffics in Olympic channels. One explanation for the increase of requests per scheduling unit is that the application works on top of UDP, which does not guarantee reliability in the way that TCP does. To conclude this subsection, we observe that adopting

6 smaller scheduling units in the Olympic channel requires more data requests, e.g., to obtain the same amount of data, the Olympic channel demands seven times more data requests than the ordinary channel does. Moreover, we observe that the expectation of the number of request per scheduling unit in the Olympic channel is nearly 44% larger than that in the ordinary channel. This produces a remarkable control overhead. However, because smaller scheduling unit increases potential suppliers for the same amount of data, the data miss ratio in the Olympic channel is significantly lower than that in the ordinary channel, leading to better playback quality in the Olympic channel. In other words, we unveil an overheadquality tradeoff, which has received little attention in the literature, as it is not as intuitive as the overhead-delay tradeoff [2]. This tradeoff sheds light on the design of future systems that can achieve a good balance. V. CONCLUSION AND FUTURE WORK P2P streaming technique is spreading widely, and the study of its characteristics on large-scale broadcasting is meaningful and important. In this paper, we conducted a measurement study on a popular P2P IPTV system, PPStream, during the 29th Olympic broadcasting, which was a large-scale event with a live interest for users. After decoding the proprietary protocol of this commercial system, we developed our dedicated measurement tool PPS-Sniffer, deployed it under different network environments, and collected extensive traces during a large number of events. Our measurement and analyses provide not only a better understanding of significant performance and design issues of PPStream, but also important insights on optimizing, modeling of such systems, and on the design of future generation of P2P IPTV systems. Specifically, our study demonstrates that:.) The playback delay is notable in Olympic channels, even for close peers. 2.) Peering strategies of PPStream target mainly on the ISP level, and should exploit peers of local cluster in order to reduce the playback lag among close peers. 3.) We unveil an overhead-quality tradeoff, which has received little attention in the literature and sheds lights on the design of future systems. To the best of our knowledge, insights gained in our study are unique and new as compared with existing studies. For future work, we are interested to explore finer granularity in peering strategies in order to improve the viewing experiences of end users nearby. Also, we plan to quantitatively demonstrate the overhead-quality tradeoff in order to provide guidelines for future designs of P2P IPTV systems. [4] X. Zhang, J. Liu, B. Li, and T.-S. P. Yum, CoolStreaming/DONet: A Data-driven Overlay Network for Peer-to-Peer Live Media Streaming, in Proc. of IEEE INFOCOM, vol. 3, pp. 22-2, Mar. 25. [5] J. Liu, S. G. Rao, B. Li, and H. Zhang, Opportunities and Challenges of Peer-to-Peer Internet Video Broadcast, (invited) Proceedings of the IEEE, Special Issue on Recent Advances in Distributed Multimedia Communications, vol. 96, no., pp. -24, Jan. 28. [6] B. Li, S. Xie, Y. Qu, G. Y. Keung, C. Lin, J. Liu and X. Zhang, Inside the New Coolstreaming: Principles, Measurements and Performance Implications, in Proc. of IEEE INFOCOM, pp.3-39, Apr. 28. [7] C. Wu, B. Li, S. Zhao. Magellan: Charting Large-Scale Peer-to-Peer Live Streaming Topologies, in Proc. of IEEE ICDCS, pp , Jun. 27. [8] C. Wu, B. Li, S. Zhao. Exploring Large-Scale Peer-to-Peer Live Streaming Topologies, to appear in ACM Transactions on Multimedia Computing, Communications and Applications, vol. 4, issue 3, Aug. 28. [9] C. Wu, B. Li, S. Zhao. Characterizing Peer-to-Peer Streaming Flows, in IEEE Journal on Selected Areas in Communications, Special Issue on Advances in Peer-to-Peer Streaming Systems, vol. 25, no. 9, pp , Dec. 27. [] X. Hei, C. Liang, J. 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Zhang, Measurement of Commercial Peer- To-Peer Live Video Streaming, in First Workshop on Recent Advances in Peer-to-Peer Streaming, Aug. 26. [6] T. Silverston and O. Fourmaux, Measuring P2P IPTV systems, in Proc. of NOSSDAV, Jun. 27. [7] The NTP website. [Online]. Available: [8] The IP to ASN Mapping. [Online]. Available: [9] Olympics broadcast regulations in CCTV.com. [Online]. Available: [2] M. Zhang, Q. Zhang, L. Sun and S. Yang, Understanding the Power of Pull-based Streaming Protocol: Can We Do Better? in IEEE Journal on Selected Areas in Communications, vol. 25, no. 9, Dec. 27. ACKNOWLEDGMENT This work has been supported by the National Natural Science Foundation of China under Grant No and REFERENCES [] The PPLive website. [Online]. Available: [2] The PPStream website. [Online]. Available: [3] The UUSee website. [Online]. Available: