Guillaume Doyen

A Survey and Synthesis of User Behavior Measurements in P2P Streaming Systems

In terms of scalability, cost and ease of deployment, the Peer-to-Peer (P2P) approach has emerged as a promising solution for video streaming applications. Its architecture enables end-hosts, called peers, to relay the video stream to each other. P2P systems are in fact networks of users who control peers. Thus, user behavior is crucial to the performance of these systems because it directly impacts the streaming flow. To understand user behavior, several measurement studies have been carried out over different video streaming systems. Each measurement analyzes a particular system focusing on specific metrics and presents insights. However, a single study based on a particular system and specific metrics is not sufficient to provide a complete model of user behavior considering all of its components and the impact of external factors on them. In this paper, we propose a comparison and a synthesis of these measurements. First of all, we review video streaming architectures, followed by a survey on the user behavior measurements in these architectures. Then, we gather insights revealed in these measurements and compare them for consensual and contrasting points. Finally, we extract components of user behavior, their external impacting factors and relationships among them. We also point out those aspects of user behavior which require further investigations.

Decentralized Aggregation Protocols in Peer-to-Peer Networks: A Survey

In large scale decentralized and dynamic networks such as Peer-to-Peer ones, being able to deal with quality of service requires the establishment of a decentralized, autonomous and efficient management strategy. In this context, there is a need to know the global state of a network by collecting a variety of distributed statistical summary information through the use of aggregation protocols. In this paper, we carried out a study on a set of aggregation protocols that can be used for autonomous network monitoring purposes in P2P networks, and we propose a classification and a comparison of them.

Detection and mitigation of localized attacks in a widely deployed P2P network

Several large scale P2P networks operating on the Internet are based on a Distributed Hash Table. These networks offer valuable services, but they all suffer from a critical issue allowing malicious nodes to be inserted in specific places on the DHT for undesirable purposes (monitoring, distributed denial of service, pollution, etc.). While several attacks and attack scenarios have been documented, few studies have measured the actual deployment of such attacks and none of the documented countermeasures have been tested for compatibility with an already deployed network. In this article, we focus on the KAD network. Based on large scale monitoring campaigns, we show that the world-wide deployed KAD network suffers large number of suspicious insertions around shared contents and we quantify them. To cope with these peers, we propose a new efficient protection algorithm based on analyzing the distribution of the peers’ ID found around an entry after a DHT lookup. We evaluate our solution and show that it detects the most efficient configurations of inserted peers with a very small false-negative rate, and that the countermeasures successfully filter almost all the suspicious peers. We demonstrate the direct applicability of our approach by implementing and testing our solution in real P2P networks.

A hierarchical architecture for a distributed management of p2p networks and services

We propose a management architecture for the P2P model which respects its distributed nature while building a hierarchical structure. This architecture enables the distribution of management functions, avoids an excessive centralization of the manager role and fits the dynamic of the P2P model well. The architecture is evaluated through an implementation in the Pastry framework.

Content pollution quantification in large P2P networks : A measurement study on KAD

Content pollution is one of the major issues affecting P2P file sharing networks. However, since early studies on FastTrack and Overnet, no recent investigation has reported its impact on current P2P networks. In this paper, we present a method and the supporting architecture to quantify the pollution of contents in the KAD network. We first collect information on many popular files shared in this network. Then, we propose a new way to detect content pollution by analyzing all filenames linked to a content with a metric based on the Tversky index and which gives very low error rates. By analyzing a large number of popular files, we show that 2/3 of the contents are polluted, one part by index poisoning but the majority by a new, more dangerous, form of pollution that we call index falsification.

Modeling user behavior in P2P live video streaming systems through a Bayesian network

Live video streaming over a Peer-to-Peer (P2P) architecture is promising due to its scalability and ease of deployment. Nevertheless, P2P-based video streaming systems still face some challenges regarding their performance. These systems are in fact overlays of users who control peers. As peers depend upon each other for receiving the video stream, the user behavior has an impact over the performance of the system. We collect the user behavior studies over live video streaming systems and identify the impact of different user activities on the performance. Based on this information, we propose a Bayesian network that models a generic user behavior initially and then adapts itself to individuals through learning from observations. We validate our model through simulations.

A CIM Extension for Peer-to-Peer Network and Service Management

Peer-to-peer (P2P) networks and services are increasingly present in the networking world. Emerging P2P based applications targeting enterprise solutions require an open approach to their management that ensures that the operation of any service is in agreement with QoS parameters. Our contribution deals with the design of a management framework for P2P networks and services. As part of this effort, we have designed a management information model that captures the different functional, organizational and topological aspects of the P2P model. It is built on the standard CIM model and is the focus of this paper.

Understanding botclouds from a system perspective: A principal component analysis

Cloud computing is gaining ground and becoming one of the fast growing segments of the IT industry. However, if its numerous advantages are mainly used to support a legitimate activity, it is now exploited for a use it was not meant for: malicious users leverage its power and fast provisioning to turn it into an attack support. Botnets supporting DDoS attacks are among the greatest beneficiaries of this malicious use since they can be setup on demand and at very large scale without requiring a long dissemination phase nor an expensive deployment costs. For cloud service providers, preventing their infrastructure from being turned into an Attack as a Service delivery model is very challenging since it requires detecting threats at the source, in a highly dynamic and heterogeneous environment. In this paper, we present the result of an experiment campaign we performed in order to understand the operational behavior of a botcloud used for a DDoS attack. The originality of our work resides in the consideration of system metrics that, while never considered for state-of-the-art botnets detection, can be leveraged in the context of a cloud to enable a source based detection. Our study considers both attacks based on TCP-flood and UDP-storm and for each of them, we provide statistical results based on a principal component analysis, that highlight the recognizable behavior of a botcloud as compared to other legitimate workloads.

Situated vs. global aggregation schemes for autonomous management systems

In the context of autonomous network management, the Autonomic Managers (AMs) need to collect management information from other elements in order to infer an overall state of the network considered by the decision making process. Two concurrent strategies are commonly used to achieve this operation. On one hand, approaches based on a situated view only gather information in a bounded neighborhood, thus providing a high reactivity to AMs for control operations. On the other hand, approaches based on a global view provide a good accuracy at the cost of a larger convergence time. Being able to choose the best approach in a given context is crucial to ensure the efficiency of an autonomous management system. Thus, in this paper, we perform an exhaustive performance analysis of these approaches by considering typical schemes of both of them, namely a one-hop and two-hops situated view against gossip- and tree-based global aggregation schemes. Metrics we consider are the convergence time, communication and computation cost, scalability and the accuracy of estimated aggregates. Given them, we show under which conditions an approach outperforms the others.

A Bayesian approach for user aware peer-to-peer video streaming systems

Peer-to-Peer (P2P) architectures for live video streaming has attracted a significant attention from both academia and industry. P2P design enables end-hosts to relay streams to each other overcoming the scalability issue of centralized architectures. However, these systems struggle to provide a service of comparable quality to that of traditional television. Since end-hosts are controlled by users, their behavior has a strong impact on the performance of P2P streaming systems, leading to potential service disruption and low streaming quality. Thus, considering the user behavior in these systems could bring significant performance improvements. Toward this end, we propose a Bayesian network that captures all the elements making part of the user behavior or related to it. This network is built from the information found in a cross-analysis of numerous large-scale measurement campaigns, analyzing the user behavior in video streaming systems. We validate our model through intensive simulations showing that our model can learn a user behavior and is able to predict several activities helping thus in optimizing these systems for a better performance. We also propose a method based on traces collection of the same user type that accelerates the learning process of this network. Furthermore, we evaluate the performance of this model through exploring its applications and comparison with non-contextual models.