Controlling P2P Traffic in Cooperative and Non-Cooperative Network Environments.

Emerging Peer-to-Peer (P2P) technologies have in the past few years enabled various types of content, such as file sharing and live video streaming, to be efficiently distributed over the Internet. However, the uncontrolled behaviour of P2P applications in consuming Internet bandwidth leads to the situation where P2P flows account for some 50%-70% of the overall Internet traffic. On the one hand, traditional Traffic Engineering (TE) techniques are deployed by Internet Service Providers (ISPs) to map traffic onto the network for achieving overall network performance optimisation, without distinguishing between standard traffic and P2P traffic. On the other hand, recent proposals have been made on the cooperation between P2P applications and the underlying ISP to not only make best use of network resources but also improve application performance, such as Application Layer Traffic Optimisation (ALTO). In this thesis we investigate how to design future intelligent Internet P2P traffic management paradigms in both a non-cooperative way (i e. an ISP-centric solution) and in a cooperative way (e.g. ALTO). Some key contributions are summarised below: 1. We investigate whether ALTO can synergistically coexist with an application-agnostic TE (AATE). We evaluate whether ALTO is an acceptable alternative for optimizing network resources, and we consider the behaviour of distinct P2P overlays (non-, semi- and fully-cooperative) coexisting with AATE, and the impact of various traffic scenarios on both application and network sides. 2. We show how to improve the performance of non-P2P services while accommodating as much P2P traffic as possible (ISP-centric solution). Since conventional Internet services (e.g. web browsing) are significantly impacted by P2P traffic and the market reputation of the ISP may be impacted if P2P traffic is absolutely blocked, we propose a dynamic P2P traffic limiting policy for ISP networks to achieve a better trade-off between ISPs and P2P systems. 3. We demonstrate significant improvement in bandwidth resource utilisation when using the cooperative approach. Due to lack of network condition information and unintelligent peer selection, current approaches deliver significant amounts of unnecessary P2P traffic. We propose an adaptive peer selection scheme that is aware of dynamic network conditions and a localised P2P traffic exchange approach to reduce P2P traffic and achieve network load balancing

Controlling P2P Traffic in Cooperative and Non-Cooperative Network Environments.

Emerging Peer-to-Peer (P2P) technologies have in the past few years enabled various types of content, such as file sharing and live video streaming, to be efficiently distributed over the Internet. However, the uncontrolled behaviour of P2P applications in consuming Internet bandwidth leads to the situation where P2P flows account for some 50%-70% of the overall Internet traffic. On the one hand, traditional Traffic Engineering (TE) techniques are deployed by Internet Service Providers (ISPs) to map traffic onto the network for achieving overall network performance optimisation, without distinguishing between standard traffic and P2P traffic. On the other hand, recent proposals have been made on the cooperation between P2P applications and the underlying ISP to not only make best use of network resources but also improve application performance, such as Application Layer Traffic Optimisation (ALTO). In this thesis we investigate how to design future intelligent Internet P2P traffic management paradigms in both a non-cooperative way (i e. an ISP-centric solution) and in a cooperative way (e.g. ALTO). Some key contributions are summarised below: 1. We investigate whether ALTO can synergistically coexist with an application-agnostic TE (AATE). We evaluate whether ALTO is an acceptable alternative for optimizing network resources, and we consider the behaviour of distinct P2P overlays (non-, semi- and fully-cooperative) coexisting with AATE, and the impact of various traffic scenarios on both application and network sides. 2. We show how to improve the performance of non-P2P services while accommodating as much P2P traffic as possible (ISP-centric solution). Since conventional Internet services (e.g. web browsing) are significantly impacted by P2P traffic and the market reputation of the ISP may be impacted if P2P traffic is absolutely blocked, we propose a dynamic P2P traffic limiting policy for ISP networks to achieve a better trade-off between ISPs and P2P systems. 3. We demonstrate significant improvement in bandwidth resource utilisation when using the cooperative approach. Due to lack of network condition information and unintelligent peer selection, current approaches deliver significant amounts of unnecessary P2P traffic. We propose an adaptive peer selection scheme that is aware of dynamic network conditions and a localised P2P traffic exchange approach to reduce P2P traffic and achieve network load balancing

A Dynamic Peer-to-Peer Traffic Limiting Policy for ISP Networks

Abstract—As a scalable paradigm for content distribution at Internet-wide scale, Peer-to-Peer (P2P) technologies have enabled a variety of networked services, such as distributed file-sharing and live video streaming. Most existing P2P systems employ nonintelligent peer selection algorithms for content swarming which greedily consume Internet bandwidth resources. As a result, Internet service providers (ISPs) need some efficient solutions for managing P2P traffic within their own networks. A common practice today is to block or shape P2P traffic in order to conserve bandwidth resources for carrying standard traffic from which revenue can be generated. In this paper, instead of looking at simple time-driven blocking/limiting approaches, we investigate how such types of limiting behaviors can be more gracefully performed by the ISP by taking into account the dynamics of both P2P traffic and of standard Internet traffic. Specifically, our approach is to adaptively limit excessive P2P traffic on critical network links that are prone to congestion, based on periodical link load/utilization measurements by the ISP. The ultimate objective is to guarantee non-P2P service capability while trying to accommodate as much P2P traffic as possible based on the available bandwidth resources. This approach can be regarded as a complementary solution to the recently proposed collaboration-based P2P paradigms such as P4P. Simulation results show that our approach not only eliminates performance degradation of non-P2P services that are caused by overwhelming P2P traffic, but also accommodates P2P traffic efficiently in both existing and future collaboration-based P2P network scenarios.

An Adaptive Peer Selection Scheme with Dynamic Network Condition Awareness

Abstract — Locality-based peer selection paradigms have been proposed recently based on cooperation between peer-to-peer (P2P) service providers, Internet Service Providers (ISPs) and end users in order to achieve efficient resource utilization by P2P traffic. Based on this cooperation between different stakeholders, we introduce a more advanced paradigm with adaptive peer selection that takes into account traffic dynamics in the operational network. Specifically, peers associated with low path utilization as measured by the ISP are selected in order to reduce the probability of network congestion. This approach not only improves real-time P2P service assurance but also optimizes the overall use of network resources. Our simulations based on the GEANT network topology and real traffic traces show that the proposed adaptive peer selection scheme achieves significant improvement in utilizing bandwidth resources as compared to static locality-based approaches. 1

Agent with Tangent-Based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound

Standard plane (SP) localization is essential in routine clinical ultrasound (US) diagnosis. Compared to 2D US, 3D US can acquire multiple view planes in one scan and provide complete anatomy with the addition of coronal plane. However, manually navigating SPs in 3D US is laborious and biased due to the orientation variability and huge search space. In this study, we introduce a novel reinforcement learning (RL) framework for automatic SP localization in 3D US. Our contribution is three-fold. First, we formulate SP localization in 3D US as a tangent-point-based problem in RL to restructure the action space and significantly reduce the search space. Second, we design an auxiliary task learning strategy to enhance the model's ability to recognize subtle differences crossing Non-SPs and SPs in plane search. Finally, we propose a spatial-anatomical reward to effectively guide learning trajectories by exploiting spatial and anatomical information simultaneously. We explore the efficacy of our approach on localizing four SPs on uterus and fetal brain datasets. The experiments indicate that our approach achieves a high localization accuracy as well as robust performance.Comment: Accepted by MICCAI 202