The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

In this paper, we develop simple models to study the performance of BitTorrent, a second generation peer-to-peer (P2P) application. We first present a simple fluid model and study the scalability, performance and efficiency of such a file-sharing mechanism. We then consider the built-in incentive mechanism of BitTorrent and study its effect on network performance. We also provide numerical results based on both simulations and real traces obtained from the Internet.

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Modeling and performance analysis of BitTorrent-like peer-to-peer networks

Peer-to-peer (P2P) file sharing accounts for an astonishing volume of current Internet traffic. This paper probes deeply into modern P2P file sharing systems and the forces that drive them. By doing so, we seek to increase our understanding of P2P file sharing workloads and their implications for future multimedia workloads. Our research uses a three-tiered approach. First, we analyze a 200-day trace of over 20 terabytes of Kazaa P2P traffic collected at the University of Washington. Second, we develop a model of multimedia workloads that lets us isolate, vary, and explore the impact of key system parameters. Our model, which we parameterize with statistics from our trace, lets us confirm various hypotheses about file-sharing behavior observed in the trace. Third, we explore the potential impact of locality-awareness in Kazaa.Our results reveal dramatic differences between P2P file sharing and Web traffic. For example, we show how the immutability of Kazaa's multimedia objects leads clients to fetch objects at most once; in contrast, a World-Wide Web client may fetch a popular page (e.g., CNN or Google) thousands of times. Moreover, we demonstrate that: (1) this "fetch-at-most-once" behavior causes the Kazaa popularity distribution to deviate substantially from Zipf curves we see for the Web, and (2) this deviation has significant implications for the performance of multimedia file-sharing systems. Unlike the Web, whose workload is driven by document change, we demonstrate that clients' fetch-at-most-once behavior, the creation of new objects, and the addition of new clients to the system are the primary forces that drive multimedia workloads such as Kazaa. We also show that there is substantial untapped locality in the Kazaa workload. Finally, we quantify the potential bandwidth savings that locality-aware P2P file-sharing architectures would achieve.

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Measurement, modeling, and analysis of a peer-to-peer file-sharing workload

A super-peer is a node in a peer-to-peer network that operates both as a server to a set of clients, and as an equal in a network of super-peers. Super-peer networks strike a balance between the efficiency of centralized search, and the autonomy, load balancing and robustness to attacks provided by distributed search. Furthermore, they take advantage of the heterogeneity of capabilities (e.g., bandwidth, processing power) across peers, which recent studies have shown to be enormous. Hence, new and old P2P systems like KaZaA and Gnutella are adopting super-peers in their design. Despite their growing popularity, the behavior of super-peer networks is not well understood. For example, what are the potential drawbacks of super-peer networks? How can super-peers be made more reliable? How many clients should a super-peer take on to maximize efficiency? we examine super-peer networks in detail, gaming an understanding of their fundamental characteristics and performance tradeoffs. We also present practical guidelines and a general procedure for the design of an efficient super-peer network.

Designing an Super-Peer Network

Many commercial video players rely on bitrate adaptation logic to adapt the bitrate in response to changing network conditions. Past measurement studies have identified issues with today's commercial players with respect to three key metrics---efficiency, fairness, and stability---when multiple bitrate-adaptive players share a bottleneck link. Unfortunately, our current understanding of why these effects occur and how they can be mitigated is quite limited.In this paper, we present a principled understanding of bitrate adaptation and analyze several commercial players through the lens of an abstract player model. Through this framework, we identify the root causes of several undesirable interactions that arise as a consequence of overlaying the video bitrate adaptation over HTTP. Building on these insights, we develop a suite of techniques that can systematically guide the tradeoffs between stability, fairness and efficiency and thus lead to a general framework for robust video adaptation. We pick one concrete instance from this design space and show that it significantly outperforms today's commercial players on all three key metrics across a range of experimental scenarios.

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Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE

Peer-peer networking has recently emerged as a new paradigm for building distributed networked applications. We develop simple mathematical models to explore and illustrate fundamental performance issues of peer-peer file sharing systems. The modeling framework introduced and the corresponding solution methods are flexible enough to accommodate different characteristics of such systems. Through the specification of model parameters, we apply our framework to three different peer-peer architectures: centralized indexing, distributed indexing with flooded queries, and distributed indexing with hashing directed queries. Using our model, we investigate the effects of system scaling, freeloaders, file popularity and availability on system performance. In particular, we observe that a system with distributed indexing and flooded queries cannot exploit the full capacity of peer-peer systems. We further show that peer-peer file sharing systems can tolerate a significant number of freeloaders without suffering much performance degradation. In many cases, freeloaders can benefit from the available spare capacity of peer-peer systems and increase overall system throughput. Our work shows that simple models coupled with efficient solution methods can be used to understand and answer questions related to the performance of peer-peer file sharing systems.

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Modeling peer-peer file sharing systems

IPTV is increasingly being deployed and offered as a commercial service to residential broadband customers. Compared with traditional ISP networks, an IPTV distribution network (i) typically adopts a hierarchical instead of mesh-like structure, (ii) imposes more stringent requirements on both reliability and performance, (iii) has different distribution protocols (which make heavy use of IP multicast) and traffic patterns, and (iv) faces more serious scalability challenges in managing millions of network elements. These unique characteristics impose tremendous challenges in the effective management of IPTV network and service.In this paper, we focus on characterizing and troubleshooting performance issues in one of the largest IPTV networks in North America. We collect a large amount of measurement data from a wide range of sources, including device usage and error logs, user activity logs, video quality alarms, and customer trouble tickets. We develop a novel diagnosis tool called Giza that is specifically tailored to the enormous scale and hierarchical structure of the IPTV network. Giza applies multi-resolution data analysis to quickly detect and localize regions in the IPTV distribution hierarchy that are experiencing serious performance problems. Giza then uses several statistical data mining techniques to troubleshoot the identified problems and diagnose their root causes. Validation against operational experiences demonstrates the effectiveness of Giza in detecting important performance issues and identifying interesting dependencies. The methodology and algorithms in Giza promise to be of great use in IPTV network operations.

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Towards automated performance diagnosis in a large IPTV network

Understanding the channel popularity or content popularity is an important step in the workload characterization for modern information distribution systems (e.g., World Wide Web, peer-to-peer file-sharing systems, video-on-demand systems).In this paper, we focus on analyzing the channel popularity in the context of Internet Protocol Television (IPTV). In particular, we aim at capturing two important aspects of channel popularity - the distribution and temporal dynamics of the channel popularity. We conduct in-depth analysis on channel popularity on a large collection of user channel access data from a nation-wide commercial IPTV network. Based on the findings in our analysis, we choose a stochastic model that finds good matches in all attributes of interest with respect to the channel popularity. Furthermore, we propose a method to identify subsets of user population with inherently different channel interest.By tracking the change of population mixtures among different user classes, we extend our model to a multi-class population model, which enables us to capture the moderate diurnal popularity patterns exhibited in some channels. We also validate our channel popularity model using real user channel access data from commercial IPTV network.

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Modeling channel popularity dynamics in a large IPTV system

The user experience for networked applications is becoming a key benchmark for customers and network providers. Perceived user experience is largely determined by the frequency, duration and severity of network events that impact a service. While today's networks implement sophisticated infrastructure that issues alarms for most failures, there remains a class of silent outages (e.g., caused by configuration errors) that are not detected. Further, existing alarms provide little information to help operators understand the impact of network events on services. Attempts to address this through infrastructure that monitors end-to-end performance for customers have been hampered by the cost of deployment and by the volume of data generated by these solutions.We present an alternative approach that pushes monitoring to applications on end systems and uses their collective view to detect network events and their impact on services - an approach we call Crowdsourcing Event Monitoring (CEM). This paper presents a general framework for CEM systems and demonstrates its effectiveness for a P2P application using a large dataset gathered from BitTorrent users and confirmed network events from two ISPs. We discuss how we designed and deployed a prototype CEM implementation as an extension to BitTorrent. This system performs online service-level network event detection through passive monitoring and correlation of performance in end-users' applications.

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Crowdsourcing service-level network event monitoring

Internet Protocol Television (IPTV) has emerged as a new delivery method for TV. In contrast with native broadcast in traditional cable and satellite TV system, video streams in IPTV are encoded in IP packets and distributed using IP unicast and multicast. This new architecture has been strategically embraced by ISPs across the globe, recognizing the opportunity for new services and its potential toward a more interactive style of TV watching experience in the future. Since user activities such as channel switches in IPTV impose workload beyond local TV or set-top box (different from broadcast TV systems), it becomes essential to characterize and model the aggregate user activities in an IPTV network to support various system design and performance evaluation functions such as network capacity planning. In this work, we perform an in-depth study on several intrinsic characteristics of IPTV user activities by analyzing the real data collected from an operational nation-wide IPTV system. We further generalize the findings and develop a series of models for capturing both the probability distribution and time-dynamics of user activities. We then combine theses models to design an IPTV user activity workload generation tool called SIMUL WATCH, which takes a small number of input parameters and generates synthetic workload traces that mimic a set of real users watching IPTV. We validate all the models and the prototype of SIMUL WATCH using the real traces. In particular, we show that SIMUL WATCH can estimate the unicast and multicast traffic accurately, proving itself as a useful tool in driving the performance study in IPTV systems.

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Zihui Ge

Papers

Modeling peer-peer file sharing systems

Towards automated performance diagnosis in a large IPTV network

Modeling channel popularity dynamics in a large IPTV system

Crowdsourcing service-level network event monitoring

Modeling user activities in a large IPTV system