Han Hee Song

Incentive-aware routing in DTNs

Disruption tolerant networks (DTNs) are a class of networks in which no contemporaneous path may exist between the source and destination at a given time. In such a network, routing takes place with the help of relay nodes and in a store-and-forward fashion. If the nodes in a DTN are controlled by rational entities, such as people or organizations, the nodes can be expected to behave selfishly and attempt to maximize their utilities and conserve their resources. Since routing is an inherently cooperative activity, system operation will be critically impaired unless cooperation is somehow incentivized. The lack of end-to-end paths, high variation in network conditions, and long feedback delay in DTNs imply that existing solutions for mobile ad-hoc networks do not apply to DTNs. In this paper, we propose the use of pair-wise tit-for-tat (TFT) as a simple, robust and practical incentive mechanism for DTNs. Existing TFT mechanisms often face bootstrapping problems or suffer from exploitation. We propose a TFT mechanism that incorporates generosity and contrition to address these issues. We then develop an incentive-aware routing protocol that allows selfish nodes to maximize their own performance while conforming to TFT constraints. For comparison, we also develop techniques to optimize the system-wide performance when all nodes are cooperative. Using both synthetic and real DTN traces, we show that without an incentive mechanism, the delivery ratio among selfish nodes can be as low as 20% as what is achieved under full cooperation; in contrast, with TFT as a basis of cooperation among selfish nodes, the delivery ratio increases to 60% or higher as under full cooperation. We also address the practical challenges involved in implementing the TFT mechanism. To our knowledge, this is the first practical incentive-aware routing scheme for DTNs.

Scalable proximity estimation and link prediction in online social networks

Proximity measures quantify the closeness or similarity between nodes in a social network and form the basis of a range of applications in social sciences, business, information technology, computer networks, and cyber security. It is challenging to estimate proximity measures in online social networks due to their massive scale (with millions of users) and dynamic nature (with hundreds of thousands of new nodes and millions of edges added daily). To address this challenge, we develop two novel methods to efficiently and accurately approximate a large family of proximity measures. We also propose a novel incremental update algorithm to enable near real-time proximity estimation in highly dynamic social networks. Evaluation based on a large amount of real data collected in five popular online social networks shows that our methods are accurate and can easily scale to networks with millions of nodes. To demonstrate the practical values of our techniques, we consider a significant application of proximity estimation: link prediction, i.e., predicting which new edges will be added in the near future based on past snapshots of a social network. Our results reveal that (i) the effectiveness of different proximity measures for link prediction varies significantly across different online social networks and depends heavily on the fraction of edges contributed by the highest degree nodes, and (ii) combining multiple proximity measures consistently yields the best link prediction accuracy.

Enabling high-bandwidth vehicular content distribution

We present VCD, a novel system for enabling high-bandwidth content distribution in vehicular networks. In VCD, a vehicle opportunistically communicates with nearby access points (APs) to download the content of interest. To fully take advantage of such transient contact with APs, we proactively push content to the APs that the vehicles will likely visit in the near future. In this way, vehicles can enjoy the full wireless capacity instead of being bottle-necked by the Internet connectivity, which is either slow or even unavailable. We develop a new algorithm for predicting the APs that will soon be visited by the vehicles. We then develop a replication scheme that leverages the synergy among (i) Internet connectivity (which is persistent but has limited coverage and low bandwidth), (ii) local wireless connectivity (which has high bandwidth but transient duration), (iii) vehicular relay connectivity (which has high bandwidth but high delay), and (iv) mesh connectivity among APs (which has high bandwidth but low coverage). We demonstrate the effectiveness of VCD system using trace-driven simulation and Emulab emulation based on real taxi traces. We further deploy VCD in two vehicular networks: one using 802.11b and the other using 802.11n, to demonstrate its effectiveness.

Detecting the performance impact of upgrades in large operational networks

Networks continue to change to support new applications, improve reliability and performance and reduce the operational cost. The changes are made to the network in the form of upgrades such as software or hardware upgrades, new network or service features and network configuration changes. It is crucial to monitor the network when upgrades are made because they can have a significant impact on network performance and if not monitored may lead to unexpected consequences in operational networks. This can be achieved manually for a small number of devices, but does not scale to large networks with hundreds or thousands of routers and extremely large number of different upgrades made on a regular basis. In this paper, we design and implement a novel infrastructure MERCURY for detecting the impact of network upgrades (or triggers) on performance. MERCURY extracts interesting triggers from a large number of network maintenance activities. It then identifies behavior changes in network performance caused by the triggers. It uses statistical rule mining and network configuration to identify commonality across the behavior changes. We systematically evaluate MERCURY using data collected at a large tier-1 ISP network. By comparing to operational practice, we show that MERCURY is able to capture the interesting triggers and behavior changes induced by the triggers. In some cases, MERCURY also discovers previously unknown network behaviors demonstrating the effectiveness in identifying network conditions flying under the radar.

NetQuest: a flexible framework for large-scale network measurement

In this paper, we present NetQuest, a flexible framework for large-scale network measurement. We apply Bayesian experimental design to select active measurements that maximize the amount of information we gain about the network path properties subject to given resource constraints. We then apply network inference techniques to reconstruct the properties of interest based on the partial, indirect observations we get through these measurements. By casting network measurement in a general Bayesian decision theoretic framework, we achieve flexibility. Our framework can support a variety of design requirements, including i) differentiated design for providing better resolution to certain parts of the network; ii) augmented design for conducting additional measurements given existing observations; and iii) joint design for supporting multiple users who are interested in different parts of the network. Our framework is also scalable and can design measurement experiments that span thousands of routers and end hosts. We develop a toolkit that realizes the framework on PlanetLab. We conduct extensive evaluation using both real traces and synthetic data. Our results show that the approach can accurately estimate network-wide and individual path properties by only monitoring within 2%-10% of paths. We also demonstrate its effectiveness in providing differentiated monitoring, supporting continuous monitoring, and satisfying the requirements of multiple users.

Q-score: proactive service quality assessment in a large IPTV system

In large-scale IPTV systems, it is essential to maintain high service quality while providing a wider variety of service features than typical traditional TV. Thus service quality assessment systems are of paramount importance as they monitor the user-perceived service quality and alert when issues occurs. For IPTV systems, however, there is no simple metric to represent user-perceived service quality and Quality of Experience (QoE). Moreover, there is only limited user feedback, often in the form of noisy and delayed customer calls. Therefore, we aim to approximate the QoE through a selected set of performance indicators in a proactive (i.e., detect issues before customers reports to call centers) and scalable fashion. In this paper, we present a service quality assessment framework, Q-score, which accurately learns a small set of performance indicators most relevant to user-perceived service quality, and proactively infers service quality in a single score. We evaluate Q-score using network data collected from a commercial IPTV service provider and show that Q-score is able to predict 60% of the service problems that are reported by customers with 0.1% false positives. Through Q-score, we have (i) gained insight into various types of service problems causing user dissatisfaction, including why users tend to react promptly to sound issues while late to video issues; (ii) identified and quantified the opportunity to proactively detect the service quality degradation of individual customers before severe performance impact occurs; and (iii) observed possibility to allocate customer care workforce to potentially troubling service areas before issues break out.

AWESoME: Big Data for Automatic Web Service Management in SDN

Software defined network (SDN) has enabled consistent and programmable management in computer networks. However, the explosion of cloud services and content delivery networks (CDNs)—coupled with the momentum of encryption—challenges the simple per-flow management and calls for a more comprehensive approach for managing Web traffic. We propose a new approach based on a “per service” management concept, which allows to identify and prioritize all traffic of important Web services, while segregating others, even if they are running on the same cloud platform, or served by the same CDN. We design and evaluate AWESoME, automatic Web service manager, a novel SDN application to address the above problem. On the one hand, it leverages big data algorithms to automatically build models describing the traffic of thousands of Web services. On the other hand, it uses the models to install rules in SDN switches to steer all flows related to the originating services. Using traffic traces from volunteers and operational networks, we provide extensive experimental results to show that AWESoME associates flows to the corresponding Web service in real-time and with high accuracy. AWESoME introduces a negligible load on the SDN controller and installs a limited number of rules on switches, hence scaling well in realistic deployments. Finally, for easy reproducibility, we release ground truth traces and scripts implementing AWESoME core components.

On Understanding User Interests through Heterogeneous Data Sources

User interests can be learned from multiple sources, each of them presenting only partial facets. We propose an approach to merge user information from disparate data sources to enable a more complete, enriched view of user interests. Using our approach, we show that merging different sources results in three times of more interest categories in user profiles than with each single source and that merged profiles can capture much more common interests among a group of users, which is key to group profiling.

TUCAN: Twitter user centric ANalyzer

Twitter has attracted millions of users that generate a humongous flow of information at constant pace. The research community has thus started proposing tools to extract meaningful information from tweets. In this paper, we take a different angle from the mainstream of previous works: we explicitly target the analysis of the timeline of tweets from “single users”. We define a framework - named TUCAN - to compare information offered by the target users over time, and to pinpoint recurrent topics or topics of interest. First, tweets belonging to the same time window are aggregated into “bird songs”. Several filtering procedures can be selected to remove stop-words and reduce noise. Then, each pair of bird songs is compared using a similarity score to automatically highlight the most common terms, thus highlighting recurrent or persistent topics. TUCAN can be naturally applied to compare bird song pairs generated from timelines of different users. By showing actual results for both public profiles and anonymous users, we show how TUCAN is useful to highlight meaningful information from a target users Twitter timeline.

Analyzing IPTV set-top box crashes

Recent advances in residential broadband access technologies have led to a wave of commercial IPTV deployments. As IPTV services are rolled out at scale, it is essential for IPTV systems to maintain ultra-high reliability and performance. A major issue that disrupts IPTV service is the crash of the set-top box (STB) software. The STB directly resides inside the consumers home network and provides the essential interface to both the user and the network to deliver rich content that goes well beyond traditional TV. To understand the potential causes of STB crashes, we perform an indepth statistical analysis focused on the relationships between STB crashes, video stream content, and user activities. Our initial results suggest that (i) impaired video streams may cause STB crashes, and (ii) continuous STB usage may gradually degrade the STB health over time.