Long Vu

Measurement and modeling of a large-scale overlay for multimedia streaming

This paper presents results from our measurement and modeling efforts on the large-scale peer-to-peer (p2p) overlay graphs spanned by the PPLive system which is arguably the most popular and largest multimedia streaming p2p system today. We believe that our findings can be used to understand large-scale p2p streaming systems for future planning of resource usage, and to provide useful and practical hints for future design of large-scale p2p streaming systems. Unlike other previous studies on PPLive, which focused on either network-centric or user-centric measurements of the system, our study is unique in (a) focusing on PPLive overlay-specific characteristics, and (b) being the first to derive mathematical models for its distributions of channel population size and session length. Our studies also reveal characteristics of multimedia streaming p2p overlays that are markedly different from existing file-sharing p2p overlays. Specifically, we find that: (1) Small PPLive overlays (as many as 500 nodes) are similar to random graphs in structure, (2) Average degree of a peer in the overlay (i.e., its out-degree) is independent of channel population size, (3) The availability correlation between PPLive peer pairs is bimodal, i.e., some pairs have highly correlated availability, while others have no correlation, (4) Unlike p2p file-sharing users, PPLive peers are impatient, (5) Session lengths (discretized, per channel) are typically geometrically distributed, (6) Channel Population Size variations are larger than in p2p file-sharing networks, yet they can be fitted with polynomial mathematical models. We conclude with a series of suggestions on how our findings can improve IPTV future design.

Jyotish: A novel framework for constructing predictive model of people movement from joint Wifi/Bluetooth trace

It is well known that people movement exhibits a high degree of repetition since people visit regular places and make regular contacts for their daily activities. This paper1 presents a novel framework named Jyotish2, which constructs a predictive model by exploiting the regular pattern of people movement found in real joint Wifi/Bluetooth trace. The constructed model is able to answer three fundamental questions: (1) where the person will stay, (2) how long she will stay at the location, and (3) who she will meet. In order to construct the predictive model, Jyotish includes an efficient clustering algorithm to exploit regularity of people movement and cluster Wifi access point information in Wifi trace into locations. Then, we construct a Naive Bayesian classifier to assign these locations to records in Bluetooth trace. Next, the Bluetooth trace with assigned locations is used to construct predictive model including location predictor, stay duration predictor, and contact predictor to provide answers for three questions above. Finally, we evaluate the constructed predictors over real Wifi/Bluetooth trace collected by 50 participants in University of Illinois campus from March to August 2010. Evaluation results show that Jyotish successfully constructs a predictive model, which provides a considerably high prediction accuracy of people movement.

Understanding overlay characteristics of a large-scale peer-to-peer IPTV system

This article presents results from our measurement and modeling efforts on the large-scale peer-to-peer (p2p) overlay graphs spanned by the PPLive system, the most popular and largest p2p IPTV (Internet Protocol Television) system today. Unlike other previous studies on PPLive, which focused on either network-centric or user-centric measurements of the system, our study is unique in (a) focusing on PPLive overlay-specific characteristics, and (b) being the first to derive mathematical models for its distributions of node degree, session length, and peer participation in simultaneous overlays. Our studies reveal characteristics of multimedia streaming p2p overlays that are markedly different from existing file-sharing p2p overlays. Specifically, we find that: (1) PPLive overlays are similar to random graphs in structure and thus more robust and resilient to the massive failure of nodes, (2) Average degree of a peer in the overlay is independent of the channel population size and the node degree distribution can be fitted by a piecewise function, (3) The availability correlation between PPLive peer pairs is bimodal, that is, some pairs have highly correlated availability, while others have no correlation, (4) Unlike p2p file-sharing peers, PPLive peers are impatient and session lengths (discretized, per channel) are typically geometrically distributed, (5) Channel population size is time-sensitive, self-repeated, event-dependent, and varies more than in p2p file-sharing networks, (6) Peering relationships are slightly locality-aware, and (7) Peer participation in simultaneous overlays follows a Zipf distribution. We believe that our findings can be used to understand current large-scale p2p streaming systems for future planning of resource usage, and to provide useful and practical hints for future design of large-scale p2p streaming systems.

Joint bluetooth/wifi scanning framework for characterizing and leveraging people movement in university campus

This paper1 presents a novel framework called UIM2, which collects both location information and ad hoc contacts of the human movement at the University of Illinois campus using Google Android phones. Each UIM experiment phone encompasses a Bluetooth scanner and a wifi scanner capturing both Bluetooth MAC addresses and wifi access point MAC addresses in proximity of the phone. Then, Bluetooth MAC addresses are used to infer contact information and the wifi MAC addresses are used to infer physical location of the phone. Using the contact and location information, we investigate first the sensitivity analysis on contact duration and inter-contact duration. Then, we characterize the regularity of people movement, visit duration of people at locations, and the popularity of locations. Finally, we present the Hybrid Epidemic data dissemination protocol, which uses both wifi access point and ad hoc contact to expedite the data forwarding. We evaluate Hybrid Epidemic protocol with our collected ad hoc and wifi traces and find that in comparison with Epidemic data dissemination protocol, the Hybrid Epidemic protocol improves data forwarding delay considerably.

Measurement of a large-scale overlay for multimedia streaming

PPLive is currently the most well-known instance of an IPTV (Internet Protocol Television) application which stands out due to its increasing popularity. As of May 2006, PPLive had over 200 distinct online channels, a daily average of 400,000 aggregated users, and most of its channels had several thousands of users at their peaks. During the Chinese New Year 2006 event, a particular PPLive channel had over 200,000 simultaneous viewers [2]. There are several measurement studies about PPLive characteristics [1, 2, 5]. These existing studies tend to predominantly look at either network-centric metrics (e.g., video traffic, TCP connections, etc.), or at user-centric metrics (e.g., geographic distribution, user arrival and departure, user-perceived quality, channel population, etc.). Our crawler-based measurement studies are unique in focusing primarily on overlay-based characteristics, which lie somewhere in between the user-centric view and the network centric view. Our studies reveal that (1) node degree is independent of channel population size, and (2) small PPLive overlays (as many as 500 nodes) are similar to random graphs in structure.

MIS: Malicious Nodes Identification Scheme in Network-Coding-Based Peer-to-Peer Streaming

Network coding has been shown to be capable of greatly improving quality of service in P2P live streaming systems (e.g., IPTV). However, network coding is vulnerable to pollution attacks where malicious nodes inject into the network bogus data blocks that are combined with other legitimate blocks at downstream nodes, leading to incapability of decoding the original blocks and substantial degradation of network performance. In this paper, we propose a novel approach to limiting pollution attacks by rapidly identifying malicious nodes. Our scheme can fully satisfy the requirements of live streaming systems, and achieves much higher efficiency than previous schemes. Each node in our scheme only needs to perform several hash computations for an incoming block, incurring very small computational latency. The space overhead added to each block is only 20 bytes. The verification information given to each node is independent of the streaming content and thus does not need to be redistributed. The simulation results based on real PPLive channel overlays show that the process of identifying malicious nodes only takes a few seconds even in the presence of a large number of malicious nodes.

Jyotish: Constructive approach for context predictions of people movement from joint Wifi/Bluetooth trace

It is well known that people movement exhibits a high degree of repetition since people visit regular places and make regular contacts for their daily activities. This paper presents a novel framework named Jyotish, which constructs a predictive model by exploiting the regularity of people movement found in the real joint Wifi/Bluetooth trace. The constructed model is able to answer three fundamental questions: (1) where the person will stay, (2) how long she will stay at the location, and (3) who she will meet. In order to construct the predictive model, Jyotish includes an efficient clustering algorithm to cluster Wifi access point information in the Wifi trace into locations. Then, we construct a Naive Bayesian classifier to assign these locations to records in the Bluetooth trace and obtain a fine granularity of people movement. Next, the fine grain movement trace is used to construct the predictive model including location predictor, stay duration predictor, and contact predictor to provide answers for three questions above. Finally, we evaluate the constructed predictive model over the real Wifi/Bluetooth trace collected by 50 participants in University of Illinois campus from March to August 2010. Evaluation results show that Jyotish successfully constructs a predictive model, which provides a considerably high prediction accuracy of people movement.

3R: Fine-grained encounter-based routing in Delay Tolerant Networks

In this paper1, we first characterize the fine-grained encounter pattern among mobile users found in a large-scale Bluetooth trace collected by 123 participants at University of Illinois campus from March to August 2010. Our characterization results show that the fine-grained encounter pattern is regular and predictable. We then design 3R routing protocol, which leverages the regularity of fine-grained encounter pattern among mobile nodes to maximize message delivery probability while preserving message delivery deadline. We evaluate and compare 3R with Prophet and Epidemic routing protocols over the collected trace. Evaluation results show that 3R outperforms other alternatives considerably by improving message delivery while reducing message overhead.

iShare: Exploiting opportunistic ad hoc connections for improving data download of cellular users

This paper1 presents an efficient sharing protocol (iShare) that blends different wireless interfaces of the mobile device for content dissemination service. With iShare, mobile users download content from a source via the cellular link and at the same time form an ad hoc mesh network for peer-to-peer exchange of content data. The mesh remains robust to network dynamics, minimizes ad hoc communication overhead, and parallelizes the downloading process among mesh members. In order to counter selfish behavior and balance the download among mesh members, we apply a practical “tit-for-tat” incentive mechanism, which exploits proximity and mutual content interest of mobile users. We simulate, evaluate the performance of iShare and compare it to other content dissemination schemes using cellular broadcast channels, cellular unicast channels, and tree-based protocols. The obtained results show that iShare significantly outperforms alternative approaches. The results also confirm that iShare enables users to continuously obtain data via ad hoc connection during the cellular handoff period, and provides multi-homing download for groups spanning adjacent cellular cells.

COADA: Leveraging Dynamic Coalition Peer-to-Peer Network for Adaptive Content Download of Cellular Users

This paper argues that Dynamic Coalition Peer-to-Peer (P2P) Network exists in numerous scenarios where mobile users cluster and form coalitions, and the relationship between sizes of coalitions and distances from mobile nodes to their Point of Interest (PoI) follows exponential distributions. The P2P coalition patterns of mobile users and their exponential distribution behavior can be utilized for efficient and adaptive content file download of cellular users. An adaptive protocol named COADA (COalition-aware Adaptive content DownloAd) is designed that (a) blends cellular and P2P (e.g., WiFi or Bluetooth) wireless interfaces, (b) leverages the clustering of people into P2P coalitions when moving towards PoI, and (c) utilizes exponential-coalition-size function of the Dynamic Coalition P2P Network to minimize the cellular download and meet content file download deadline. With COADA protocol, mobile nodes periodically sample the current P2P coalition size and predict the future coalition size using the exponential function. In order to decide how much file data is available in P2P coalition channels versus how much file data must be downloaded from the server over the cellular network, Online Codes techniques are used and tune cellular download timers to meet the file download deadline. The simulation results show that COADA achieves considerable performance improvements by downloading less file data from the cellular channel and more file data over the P2P coalition network while meeting the file download deadline.