OAZE: A network-friendly distributed zapping system for peer-to-peer IPTV

IPTV systems attracting millions of users are now commonly deployed on peer-to-peer (P2P) infrastructures and provide an appealing alternative to multicast-based systems. Typically, a P2P overlay network is associated with each channel, composed of users who receive, watch and redistribute this channel. Yet, channel surfing (aka as zapping) involves switching overlays and may introduce delays, potentially hurting the user experience when compared to multicast-based IPTV. In this paper, we present a distributed system called OAZE (Overlay Augmentation for Zapping Experience) which speeds up the switching process and reduces the overall cross-domain traffic generated by the IPTV system. In OAZE, each peer maintains connections to other peers, not only in a given channel, but also in a subset of all channels to which the associated user is likely to zap. More specifically, we focus on the channel assignment problem, i.e. determining, in a given P2P overlay, the optimal distribution of the responsibility to maintain contact peers to other channels. We propose an approximate algorithm providing guaranteed performances, and a simpler and more practical one. Our experimental results show that OAZE leads to substantial improvements on the connections between peers, resulting in less switching delay and lower network cost; it then represents an appealing add-on for existing P2P IPTV systems.     

Characterization of community based-P2P systems and implications for traffic localization

In this paper, we present one of the first and most extensive characterizations of closed community-based P2P systems. Such systems are organic groups of peer-to-peer (P2P) clients, which can be joined only by users belonging to a certain network (e.g., connected to a given Internet Service Provider (ISP)). A number of factors motivate the growth of these communities, such as quality of content, anonymity of transfers, and the potential for better performance that enhances user experience. Our study is conducted in two contrasting environments—a campus network and a national ISP—located in different continents. In both cases, large-scale closed communities have been found to be the predominant P2P systems in use. We shed light both on the factors motivating the growth of such communities, and present results characterizing the extensiveness of their usage, the performance achievable by the systems, and the implications of such communities for network providers. While our findings are interesting in their own right, they also offer important lessons for ongoing research that seeks to localize traffic within ISP boundaries. In particular, our results suggest that (i) in ISPs with heterogeneous access technologies, the performance benefits to users on localizing P2P traffic is largely dependent on the degree of seed-like behavior of peers behind high-bandwidth access technologies; and (ii) while localization can reduce the traffic on Internet peering links, it has the potential to cause a significant increase in traffic on internal links of providers, potentially requiring upgrades of network links.     

Modeling and evaluating IPTV applications in WiMAX networks

Internet Protocol-based Television (IPTV) is a digital television service which delivers television content via an IP network. The rapid growth of wireless network technology in recent years has changed, the way people access the Internet. Adding mobility to IPTV can create a truly compelling ubiquitous service which spans different network domains and varied IP-enabled terminals and devices, such as set-top boxes, PCs and cell phones. However, extending IPTV service to wireless networks requires overcoming bandwidth bottlenecks and high packet loss rates. Following the IEEE 802.16 standard, worldwide interoperability for microwave access (WiMAX) features high data rates and large service coverage, offering a wireless broadband solution for IPTV services. While previous research has focused on creating a broadband IPTV service few studies have practically evaluated IPTV applications in a wireless broadband network environment. In this paper, we model and evaluate a common constant bit rate (CBR)¹ based IPTV application and an IPTV live streaming (PPStreaming)² application while retrieving IPTV content via a WiMAX network. We also use the NS2 simulation tool to evaluate the performance of these two IPTV applications. The evaluation metrics include latency, packet loss, data rate and throughput statistics when the two IPTV applications are run in the WiMAX network. ¹The simplest IPTV solution is to convey video content by CBR. IPTV operators and content delivery networks relay CBR streaming content to control the demand for network capacity. Broadcasters prefer CBR video as it conserves bandwidth resources, but CBR delivery can degrade video quality result in higher overall demand on network capacity. ²PPStreaming (also referred to as P2P streaming Internet TV) is a network for live media streaming. In principle it’s similar to BitTorrent (BT) in that it provides stable and smooth broadcast of TV programs to broadband users. Unlike traditional streaming media, PPStreaming adopts P2P-streaming technology and supports full-scale visits with tens of thousands of simultaneous users. Its client software can be used in the browser or as a standalone executable.     

A Measurement Study of a Large-Scale P2P IPTV System

An emerging Internet application, IPTV, has the potential to flood Internet access and backbone ISPs with massive amounts of new traffic. Although many architectures are possible for IPTV video distribution, several mesh-pull P2P architectures have been successfully deployed on the Internet. In order to gain insights into mesh-pull P2P IPTV systems and the traffic loads they place on ISPs, we have undertaken an in-depth measurement study of one of the most popular IPTV systems, namely, PPLive. We have developed a dedicated PPLive crawler, which enables us to study the global characteristics of the mesh-pull PPLive system. We have also collected extensive packet traces for various different measurement scenarios, including both campus access networks and residential access networks. The measurement results obtained through these platforms bring important insights into P2P IPTV systems. Specifically, our results show the following. 1) P2P IPTV users have the similar viewing behaviors as regular TV users. 2) During its session, a peer exchanges video data dynamically with a large number of peers. 3) A small set of super peers act as video proxy and contribute significantly to video data uploading. 4) Users in the measured P2P IPTV system still suffer from long start-up delays and playback lags, ranging from several seconds to a couple of minutes. Insights obtained in this study will be valuable for the development and deployment of future P2P IPTV systems.     

Monitoring, analyzing and characterizing lookup traffic in a large-scale DHT

Lookup is crucial to locate peers and resources in structured P2P networks. In this paper, we measure and analyze the traffic characteristics of lookup in Kad, which is a widely used DHT network. Some previous works studied the user behaviors of Kad, yet we believe that investigating its traffic characteristics will also be beneficial, as it gives feedbacks to fine tune the system parameters, helps to uncover the abnormalities or misuses, and provides solid ground for synthesizing P2P traffic to evaluate future designs.To track the lookup requests more efficiently and from more peers in Kad, we develop an active traffic monitor, named Rememj. From the one-week data it collected, we uncover some interesting phenomena. Moreover, we characterize the traffic characteristics from the collected data in a form that can be used for constructing representative synthetic workloads for evaluating DHT optimizations or designs. In particular, the analysis exposes heterogeneous behavior that occurs in different geographical regions (i.e., Europe, Asia, and America) or during different periods of the day. The workload measures include distribution of peers, distribution of request load, distribution of targets, as well as similarity among targets.     

IPTV平台实时检测系统的设计与实现

基于P2P的网络电视直播(IPTV)业务快速发展并带来诸多安全隐患,精确检测IPTV流使用的平台是对其实施有效监管的前提。为此,采用支持向量机方法,依据网络流量的流量特征和行为特征,选取PPlive、PPstream、QQlive、Sopcast和UUsee作为IPTV平台的代表,设计并实现一种IPTV平台实时检测系统。实验结果证明,该系统能够在5 min内检测出被管理节点是否在使用IPTV平台及平台类型,平均检测精度为90.62%。     

Towards capacity and profit optimization of video-on-demand services in a peer-assisted IPTV platform

This paper studies the conditions under which peer-to-peer (P2P) technology may be beneficial in providing IPTV services over typical network architectures. It has three major contributions. First, we contrast two network models used to study the performance of such a system: a commonly used logical “Internet as a cloud” model and a “physical” model that reflects the characteristics of the underlying network. Specifically, we show that the cloud model overlooks important architectural aspects of the network and may drastically overstate the benefits of P2P technology. Second, we propose an algorithm called Zebra that pre-stripes content across multiple peers during idle hours to speed up P2P content delivery in an IPTV environment with limited upload bandwidth. We also perform simulations to measure Zebra’s effectiveness at reducing load on the content server during peak hours. Third, we provide a cost-benefit analysis of P2P video content delivery, focusing on the profit trade-offs for different pricing/incentive models rather than purely on capacity maximization. In particular, we find that under high volume of video demand, a P2P built-in incentive model performs better than any other model, while the conventional no-P2P model generates more profits when the request rate is low. The flat-reward model generally falls in between the usage-based model and the built-in model in terms of profitability except for low request rates. We also find that built-in and flat-reward models are more profitable than the usage-based model for a wide range of subscriber community sizes. Funding for J. Rahe’s research has been provided by AT&T Labs, the State of California under the MICR Oprogram, and by the Toshiba Corporation. Zhen Xiao is partially supported by China MOST project (2006BAH02A10).     

Clustering botnet communication traffic based on n-gram feature selection

Recognized as one the most serious security threats on current Internet infrastructure, botnets can not only be implemented by existing well known applications, e.g. IRC, HTTP, or Peer-to-Peer, but also can be constructed by unknown or creative applications, which makes the botnet detection a challenging problem. Previous attempts for detecting botnets are mostly to examine traffic content for bot command on selected network links or by setting up honeypots. Traffic content, however, can be encrypted with the evolution of botnet, and as a result leading to a fail of content based detection approaches. In this paper, we address this issue and propose a new approach for detecting and clustering botnet traffic on large-scale network application communities, in which we first classify the network traffic into different applications by using traffic payload signatures, and then a novel decision tree model is used to classify those traffic to be unknown by the payload content (e.g. encrypted traffic) into known application communities where network traffic is clustered based on n-gram features selected and extracted from the content of network flows in order to differentiate the malicious botnet traffic created by bots from normal traffic generated by human beings on each specific application. We evaluate our approach with seven different traffic trace collected on three different network links and results show the proposed approach successfully detects two IRC botnet traffic traces with a high detection rate and an acceptable low false alarm rate.     

Characterization of signaling and traffic in Joost

Peer-to-Peer (P2P) IPTV applications have increasingly been considered as a potential approach to online broadcasting. Recently, many applications such as PPlive, PPStream, and Sopcast have been deployed to deliver live streaming via P2P. One of the latest systems is Joost, which can deliver both Video-on-Demand and Real-Time services. Measuring and characterizing this application in terms of signaling overheads and traffic profiles helps to better understand the key limitations of current P2P IPTV systems. Therefore, the main purpose of this paper is firstly to study the impact of Joost on the network. Secondly, we wish to determine the underlying mechanisms of Joost, distinguishing between the Video-on-Demand and the Real-time services. Our study is carried out through a close investigation and analysis on the traffic of Joost in two types of streaming. Based upon the data tracing and collection, many different statistics have been derived. Our study unveils strengths (e.g. good resilience to end-to-end delay and jitter) and shortcomings (e.g. poor locality) and yields recommendations for future P2P IPTV systems.

CUFTI: Methods for core users finding and traffic identification in P2P systems

Peer-to-Peer system has achieved great success with millions of end users in the past several years. P2P traffic has occupied about 60–80 % of the total traffic volumes, which greatly consumes network bandwidth and causes congestions. To achieve the goal of efficacious P2P system management in the monitored network, in this paper we develop a framework named CUFTI (Core Users Finding and Traffic Identification). The core users are referred as long-lived peers, and we focus on life-time characteristics of coexisting peers within each snapshot of the overlay. Based on the analysis results of user’s behaviour in PPlive system, we develop an accurate model to forecast the peer’s residual life-time and identify the long-lived peers. Furthermore, we develop a flow identification model for P2P traffic management of those core users. Based on the analysis results of actual traffic traces, we find the P2P traffic flows are composed of data and control packets. Most of the control packets appear at the beginning and end of each flow to establish and close the communication between peers. We employ the direction and payload length of the control packets at the beginning of the flow as features to perform flow identification. Experimental results based on traces collected from the Northwest Region Center of CERNET (China Education and Research Network) show that the newly developed methods outperforms other existing methods with lower false negative rate (FNR) and false positive rate (FPR).     

PeerStreaming: design and implementation of an on-demand distributed streaming system with digital rights management capabilities

We have developed PeerStreaming, an on-demand peer-to-peer (P2P) media streaming system. The behavior of a PeerStreaming client is very much like an ordinary media player: it streams and plays whatever media the user desires, starting at whatever point he/she desires. Under the hood, however, the media is delivered from multiple peers to the client. The serving peers can be the media server, but more commonly they are the clients that have received the media in a previous streaming session. Leveraging the network and storage resources of the P2P network and retrieving a major part of the media from nearby peers, PeerStreaming greatly improves streaming media quality, reduces server load, and eases traffic on the network. We describe the design and implementation of the PeerStreaming system in this paper, with a focus on a number of key components. They include: (1) peer discovery and content location, (2) scalable coded and randomly accessible media format, (3) receiver-driven streaming, (4) digital rights management, and (5) architecture for the media rendering, caching and serving system.     

Characterizing peers communities and dynamics in a P2P live streaming system

Despite the large number of works devoted to understand P2P live streaming applications, most of them put forth so far rely on characterizing the static view of these systems. In this work, we characterize the SopCast, one of the most important P2P live streaming applications. We focus on its dynamics behavior as well as on the community formation phenomena. Our results show that SopCast presents a low overlay topology diameter and low end-to-end shortest path. In fact, diameter is smaller than 6 hops in almost 90 % of the observation time. More than 96 % of peers’ end-to-end connections present only 3 hops. These values combined may lead to low latencies and a fast streaming diffusion. Second, we show that communities in SopCast are well defined by the streaming data exchange process. Moreover, the SopCast protocol does not group peers according to their Autonomous System. In fact, the probability that a community contains 50 % of its members belonging to the same AS (when we observe the largest AS of our experiments) is lower then 10 %. Peers exchange more data with partners belonging to the same community instead of peers inside the same AS. For the largest AS we have, less than 18 % of peer traffic has been exchanged with another AS partners. Finally, our analysis provides important information to support the future design of more efficient P2P live streaming systems and new protocols that exploit communities’ relationships.     

Classifying peer-to-peer applications using imbalanced concept-adapting very fast decision tree on IP data stream

Peer-to-Peer (P2P) applications generate streaming data in large volumes, where new communities of peers regularly attend and existing communities of peers regularly leave, requiring the classification techniques to consider concept drift, and update the model incrementally. Concept-adapting Very Fast Decision Tree (CVFDT) is one of the well-known streaming data mining techniques that can be applied to P2P traffic. However, we observe that P2P traffic data is class imbalanced, namely, only about 30 % of examples can be labeled as “P2P”, biasing the trained models (e.g. decision tree) towards the majority class (i.e. “NonP2P”). In this paper, we propose a new technique, the imbalanced CVFDT (iCVFDT), by integrating the CVFDT with an efficient resampling technique to address the issue of the class imbalanced data. The iCVFDT classification technique enjoys the advantages of CVFDT (such as stability), and at the same time, is not sensitive to imbalanced data. We captured the Internet traffic at a main gateway and prepared a real data stream with 3.5 million examples to which the iCVFDT classification technique was applied. The experimental results demonstrate a significant improvement in the performance of the iCVFDT compared to that of the CVFDT.     

OpenIPTV: a comprehensive SDN-based IPTV service framework

IPTV is an emerging TV content delivery service that should guarantee Quality of Service (QoS) to deliver television contents over IP for their customers. However, providing such QoS regarding service level agreements (SLA) requires frequent service monitoring and adaptive configuration mechanisms. Nowadays, Software Defined Networks (SDNs) provide capabilities to deploy and manage networks dynamically and can maintain QoS. In this paper, a novel IPTV service framework (OpenIPTV) is proposed, which utilizes SDN as an underlying technology for providing QoS for IPTV customers in a shared backbone network. OpenIPTV is implemented in a well-known OpenDayLight controller and strictly followed a modular design for the sake of efficiency. OpenIPTV comprises all service requirements such as resource monitoring, channel changing, multicast group managing and dynamic QoS multicast traffic engineering. The performance of OpenIPTV is evaluated under different scenarios and experimental results confirmed the effectiveness of the proposed framework in terms of QoS metrics. Furthermore, experimental results show that OpenIPTV is a feasible and practical solution to deliver IPTV services with high level of QoS over SDN.     

Adaptive IPTV services based on a novel IP Multimedia Subsystem

Heterogeneous communication devices are emerging and changing the way of communication. Innovative multimedia applications are now accessible through these embedded systems. The 3GPP IP Multimedia Subsystem (IMS) provides a basic architecture framework for the Next Generation Network (NGN) supporting the convergence platform for service provisioning in heterogeneous networks. ETSI TISPAN standardization effort focuses on delivering IPTV services on such platform. Nevertheless, IPTV on IMS standardization suffers from a lack of efficient user-centric network management mechanisms as the end-user may consume IPTV service from different access networks, on different mobile devices, at anytime. User’s Perceived Quality of Service (PQoS) or Quality of Experience (QoE) of IPTV service may also suffer from wireless access network impairments. This paper introduces new functionalities in IPTV over IMS architecture which optimize satisfaction of the end-user and resource utilization of the operator’s networks. A context-sensitive User Profile (UP) model is used to deliver IPTV streams adapted to the user’s environment. In order to optimize the operator network usage, the impact of spatiotemporal dynamics of the video content on the deduced perceptual quality is considered. A Multimedia Content Management System (MCMS) is proposed to perform dynamic cross-layer adaptation of the IPTV stream based on PQoS measurements at the end-user side.     

TRUST BY ASSOCIATION: A META‐REPUTATION SYSTEM FOR PEER‐TO‐PEER NETWORKS

Trust mechanisms are used in peer‐to‐peer (P2P) networks to help well‐behaving peers find other well‐behaving peers with which to trade. Unfortunately, these trust mechanisms often do little to keep badly behaving peers from entering and taking advantage of the network, which makes the resulting network difficult or impossible to use for legitimate purposes such as e‐commerce. We propose trust by association, a way of tying peers together in invitation‐only P2P networks in such a way as to encourage the removal of badly behaving peers. We use invitations to create a structure within the otherwise ad hoc P2P network. Using this structure, we create a meta‐reputation system where we measure a peer’s trustworthiness not only by its own behavior, but also by the behavior of the peers it has invited to join. The connection created between the peers takes advantage of the external social relationship that must exist before a peer can be invited into the network. The result is a P2P network where, rather than just trying to marginalize badly behaving peers, there is incentive to kick them out of the network. We present results from a simple simulation showing that our approach works well in general when combined with and compared to an existing trust mechanism.     

Measurement-based study on the relation between users’ watching behavior and network sharing in P2P VoD systems

P2P-based Video on Demand (P2P VoD) systems can provide higher quality video services than P2P live systems. However, owing to the unavailability of open source codes, the characteristics of P2P VoD systems have not been fully explored. For this purpose, in this paper, we first implemented reverse engineering to the protocols of two most popular VoD systems used in China. Peers’ watching behavior and network sharing were then studied through the measured buffer messages. Based on our study, we found that uploading-only peers were more than downloading peers, and smooth-watching peers were more than randomly-seeking peers in the current P2P VoD systems. This finding is significantly different from the existing P2P-based file dissemination systems and the results obtained in the previous work on VoD systems. Furthermore, this study revealed the relations between watching behavior and network sharing. It has been observed that randomly-seeking peers will provide more regular as well as rare chunks than smooth-watching peers. A simple mathematical model was established to analytically demonstrate the relation between the watching index (WI) distribution and network-sharing profile, and this relation was also validated by our measured data.     

基于爬行器的大规模P2P IPTV测量

为了解大规模P2P IPTV系统中的用户行为特征和拓扑结构特征等内在信息,开发和部署了一个多协议P2P IPTV爬行器TVCrawer,对3个主流的P2P IPTV系统--PPLive,PPStream和UUSee进行了大量的主动测量,并对P2P IPTV系统中的用户行为和网络拓扑特征进行了分析和比较.主要发现包括:1...     

On incentivizing upload capacity in P2P-VoD systems: Design,analysis and evaluation

Free riding has long been a serious problem in peer-to-peer (P2P) systems due to the selfish behavior of individual users. To conquer this problem, a key design issue of the P2P systems is to appropriately incentivize users to contribute resources. In P2P Video-on-Demand (VoD) applications, content providers need to incentivize the peers to dedicate bandwidth and upload data to one other so as to alleviate the upload workload of their content servers. In this paper, we design a simple yet practical incentive mechanism that rewards each peer based on its dedicated upload bandwidth. We use a mean field interaction model to characterize the distribution of number of peers in different video segments, based on which we characterize the content providers’ uploading cost as a function of the peers’ contribution. By using a game theoretic framework, we analyze the interaction between a content provider’s rewarding strategy and the peers’ contributing behaviors and derive a unique Stackelberg equilibrium. We further analyze the system efficiency in terms of the price of anarchy and study the long term behavior of the system under a repeated game setting. Via extensive simulations, we validate the stability and efficiency of our incentive scheme.     

面向对等社区的信誉证据服务

对等社区是一类支持网络节点对等的进行信息交换和交易的分布式系统。单个网络节点通常参与多个对等社区,但是现有信誉系统仅根据社区内部证据计算节点信誉,忽略了信誉只有综合考虑其参与的所有社区中的证据才能得到这一社会特性。提出了一种新的信誉组合服务体系结构RepCom在众多社区间实现相关信誉证据的安全收集和管理。RepCom引入一种信誉权威网络实现证据的发布、搜索和获取,并利用逻辑引擎实现基于社区的证据信任策略。RepCom有助于构造全新的通用信誉管理服务,使信誉能够反映节点在不同社区的全局行为。