A game theory framework for ISP streaming traffic management

The overlay/underlay topology mismatch affects the performance of existing P2P platforms that can generate large volumes of unnecessary inter-ISP network traffic. Although recent works have shown the benefits of network awareness P2P solutions, no studies have focused on the investigation of the ISP behavior and their cooperative/non-cooperative attitudes.This paper proposes a game theoretic framework to help the design of techniques promoting the ISP cooperation in P2P streaming platforms and decreasing unnecessary inter-domain streaming traffic.We first analyze some simple scenarios to discuss the existence of Nash equilibria, the Pareto optimality, and a fairness criterion to refine the equilibrium points. Moreover, we apply ideas from Evolutionary Game Theory to design a distributed schemata that the ISPs can use to reach “socially acceptable” equilibrium points in a large ISP population. Furthermore, we develop a discrete event simulation to evaluate the effectiveness of the Evolutionary Game Theory framework.The study presented in the paper shows that the proposed strategies can effectively stimulate ISP cooperation aiming at the minimization of inter-ISP traffic and help to provide reliable P2P streaming service.     

A unified benchmarking and model-based framework for building QoS-aware streaming media services

A number of technology and workload trends motivate us to consider the appropriate resource allocation mechanisms and policies for streaming media services in shared cluster environments. We present MediaGuard – a model-based infrastructure for building streaming media services – that can efficiently determine the fraction of server resources required to support a particular client request over its expected lifetime. The proposed solution is based on a unified cost function that uses a single value to reflect overall resource requirements such as the CPU, disk, memory, and bandwidth necessary to support a particular media stream based on its bit rate and whether it is likely to be served from memory or disk. We design a novel, time-segment-based memory model of a media server to efficiently determine in linear time whether a request will incur memory or disk access when given the history of previous accesses and the behavior of the server's main memory file buffer cache. Using the MediaGuard framework, we design two media services: (1) an efficient and accurate admission control service for streaming media servers that accounts for the impact of the server's main memory file buffer cache, and (2) a shared streaming media hosting service that can efficiently allocate the predefined shares of server resources to the hosted media services, while providing performance isolation and QoS guarantees among the hosted services. Our evaluation shows that, relative to a pessimistic admission control policy that assumes that all content must be served from disk, MediaGuard (as well as services that are built using it) deliver a factor of two improvement in server throughput.     

A modeling framework of content pollution in Peer-to-Peer video streaming systems

Peer-to-Peer (P2P) live video streaming systems are known to suffer from intermediate attacks due to its inherent vulnerabilities. The content pollution is one of the common attacks that have received little attention in P2P live streaming systems. In this paper, we propose a modeling framework of content pollution in P2P live streaming systems. This model considers both unstructured and structured overlays, and captures the key factors including churns, user interactions, multiple attackers and defensive techniques. The models are verified with simulations and implemented in a real working system, Anysee. We analyze content pollution and its effect in live streaming system. We show that: (1) the impact from content pollution can exponentially increase, similar to the random scanning worms, leading to playback interruption and unnecessary bandwidth consumption; (2) content pollution is influenced by peer cooperation, peer degree and bandwidth in unstructured overlays, and topology breadth in structured ones; (3) the structured overlay is more resilient to content pollution; (4) a hybrid overlay result in better reliability and pollution resistance; (5) hash-based chunk signature scheme is most promising against content pollution.     

A grid density based framework for classifying streaming data in the presence of concept drift

Mining data streams is the process of extracting information from non-stopping, rapidly flowing data records to provide knowledge that is reliable and timely. Streaming data algorithms need to be one pass and operate under strict limitations of memory and response time. In addition, the classification of streaming data requires learning in an environment where the data characteristics might change constantly. Many of the classification algorithms presented in literature assume a 100 % labeling rate, which is impractical and expensive when data records are rapidly flowing in. In this paper, a new incremental grid density based learning framework, the GC3 framework, is proposed to perform classification of streaming data with concept drift and limited labeling. The proposed framework uses grid density clustering to detect changes in the input data space. It maintains an evolving ensemble of classifiers to learn and adapt to the model changes over time. The framework also uses a uniform grid density sampling mechanism to obtain a uniform subset of samples for better classification performance with a lower labeling rate. The entire framework is designed to be one-pass, incremental and work with limited memory to perform any-time classification on demand. Experimental comparison with state of the art concept drift handling systems demonstrate the GC3 frameworks ability to provide high classification performance, using fewer models in the ensemble and with only 4-6 % of the samples labeled. The results show that the GC3 framework is effective and attractive for use in real world data stream classification applications.     

SVCEval-RA: an evaluation framework for adaptive scalable video streaming

Multimedia content adaption strategies are becoming increasingly important for effective video streaming over the actual heterogeneous networks. Thus, evaluation frameworks for adaptive video play an important role in the designing and deploying process of adaptive multimedia streaming systems. This paper describes a novel simulation framework for rate-adaptive video transmission using the Scalable Video Coding standard (H.264/SVC). Our approach uses feedback information about the available bandwidth to allow the video source to select the most suitable combination of SVC layers for the transmission of a video sequence. The proposed solution has been integrated into the network simulator NS-2 in order to support realistic network simulations. To demonstrate the usefulness of the proposed solution we perform a simulation study where a video sequence was transmitted over a three network scenarios. The experimental results show that the Adaptive SVC scheme implemented in our framework provides an efficient alternative that helps to avoid an increase in the network congestion in resource-constrained networks. Improvements in video quality, in terms of PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index) are also obtained.     

A unified discrete framework for intrinsic and extrinsic Dirac operators for geometry processing

Spectral mesh analysis and processing methods, namely ones that utilize eigenvalues and eigenfunctions of linear operators on meshes, have been applied to numerous geometric processing applications. The operator used predominantly in these methods is the Laplace‐Beltrami operator, which has the often‐cited property that it is intrinsic, namely invariant to isometric deformation of the underlying geometry, including rigid transformations. Depending on the application, this can be either an advantage or a drawback. Recent work has proposed the alternative of using the Dirac operator on surfaces for spectral processing. The available versions of the Dirac operator either only focus on the extrinsic version, or introduce a range of mixed operators on a spectrum between fully extrinsic Dirac operator and intrinsic Laplace operator. In this work, we introduce a unified discretization scheme that describes both an extrinsic and intrinsic Dirac operator on meshes, based on their continuous counterparts on smooth manifolds. In this discretization, both operators are very closely related, and preserve their key properties from the smooth case. We showcase various applications of our operators, with improved numerics over prior work.     

VRML animated model watermarking scheme using geometry and interpolator nodes

Distinct from image and video watermarking, a watermarking scheme for 3D animation content is required in the 3D industry market for various applications. This paper develops a watermarking scheme for copyright protection and authentication of 3D animation content. A 3D animated model generally has a hierarchical structure with a number of transform nodes of a geometry node and an interpolator node for the timeline in contrast to a 3D polygon mesh model. The proposed scheme embeds not only a robust watermark into the geometry node for copyright protection but also a fragile watermark into the position and orientation interpolators for content authentication. We named the former “robust geometry watermarking” and the latter “fragile interpolator watermarking”. The proposed scheme performs the two watermarking schemes independently to realize simultaneously robust and fragile watermarked 3D animated model. Experimental results confirm that a watermark embedded by geometry watermarking robust to many attacks from commercial 3D editing tools while a watermark embedded by interpolator watermarking fragile to the same attacks.     

Towards building large scale live media streaming framework for a U-city

This paper proposes camera and media stream management techniques at the middleware level for implementing a U-City (ubiquitous city). The study focuses on overcoming the difficulties associated with developing middleware capable of processing and streaming multimedia data from a large number of cameras by expanding the traditional media processing technology. The content of the study can be classified into two main categories: One is a camera array management technique that involves the middleware-level framework and protocol for managing the camera array. The other is the media stream management technique for effective delivery management and processing of the multimedia streams from the camera array.

A Collaborative Scene Editor for VRML Worlds

In this paper, we analyze the requirements for a Web-based collaborative infrastructure within a virtual world. Additionally, we combine several tools and methodologies to propose a flexible and fluid collaborative environment using Java language to create a VRML scene graph. The proposed prototype aims at four aspects: a shared workspace of scene editor, an active entity composition algorithm in Java, collaborative control in the multi-user environment, and access control mechanism toward the shared data.     

3DStreaming: an open-source flexible framework for real-time 3D streaming services

The progress of three-dimensional 3D technologies, together with the wide diffusion of both Internet and broadband technologies, is paving the way to emerging live streaming services which have been conceived for delivering 3D video contents in real-time fashion to end users. Nowadays, the only available tools supporting stereoscopic 3D video services cannot be freely downloaded and require the adoption of owner stereoscopic players. Motivated by the lack of an effective solution, we developed a freeware and open source 3D live streaming framework, namely 3DStreaming. It provides stereoscopic 3D live streaming services over the Internet. In particular, it realizes a complete server implementation, offering the support for any transmission protocol and encoding scheme, as well as the full compatibility with any network architecture (i.e., LAN, MAN, Internet, and so on). At the same time, it allows users to use the preferable stereoscopic player and to render the video through any technique available for the chosen player. The overall performances of the proposed tool have been presented by testing its behavior in several network configurations (i.e., by varying network topology, coding technique, 3D representation format, and average encoding rate). All the measured metrics, which include the number of RTP segments that are transmitted and received, the frame loss ratio, and the PSNR, fully demonstrate the right behavior of the implemented tool in all the considered scenarios. We believe that, thanks to its high flexibility, this tool can be exploited by researchers working on stereoscopic-3D related issues to design, test, and evaluate novel and innovative algorithms, protocols, and network architectures.     

JCOGIN: a programming framework for particle transport on combinatorial geometry

The Journal of Supercomputing - Domain-specific programming frameworks are usually effective to simplify the development of large-scale applications on supercomputers. This paper introduces a...     

一种适合VRML应用的三角网格简化算法

交互虚拟环境中，VRML作为三维场景描述语言得到广泛应用，场景中的物体常常用三角形网格模型来描述，本文提出了一种适合VRML应用的网格简化算法，该算法不仅可以快速减少模型中的画片数目而且能保持模型良好的视觉效果，算法中给出了一种有效的误差控制方法，能在用户指定的误差范围内通过使原始网格中的边折叠达到大量简化的目的，该算法实现简单且速度快，另外能够有效地支持细节层次模型的表示，最后给出实例证明了该算法的有效性。     

A general streaming algorithm for pattern discovery

Discovering frequent patterns over event sequences is an important data mining problem. Existing methods typically require multiple passes over the data, rendering them unsuitable for streaming contexts. We present the first streaming algorithm for mining frequent patterns over a window of recent events in the stream. We derive approximation guarantees for our algorithm in terms of: (i) the separation of frequent patterns from the infrequent ones, and (ii) the rate of change of stream characteristics. Our parameterization of the problem provides a new sweet spot in the tradeoff between making distributional assumptions over the stream and algorithmic efficiencies of mining. We illustrate how this yields significant benefits when mining practical streams from neuroscience and telecommunications logs.     

A proxy architecture for collaborative media streaming

Streaming media from the Internet is a successful application for end-users. With the upcoming success of mobile devices and home networking environments, cooperation among users will become more important in the future. To achieve such cooperation, explicit middleware standards have been defined. On the other hand, Internet conferencing applications do not handle collaborative streaming sessions with individual control for each user. We propose a new concept for cooperation exemplary for collaborative media streaming using IETF multimedia session control protocols together with a proxy architecture. This concept enables both synchronization among clients and flexible control to individual users.     

Aesthetic geometry evolution in a generic interactive evolutionary design framework

An interactive evolutionary design framework is presented which provides a testbed for the development and exploration of a range of visual aesthetic design spaces. A commercial 3D digital content development software package is used to leverage the strength of existing 3D algorithm implementations and familiar interfaces and tools. The domain of non-representational 3D forms sculpted with a series of non-linear deformers is discussed as the primary example. Additional simple problem domains are shown which are being developed to serve as examples for non-programmer artists and designers who are familiar with digital content authoring in Maya. These examples will enable such individuals to rapidly construct their own interactive evolution systems. Matthew Lewis, Ph.D.: He is a researcher at The Advanced Computing Center for the Arts and Design (ACCAD) at The Ohio State University, where he received his M.S. (1993) and Ph.D. (2001) degrees in Computer Science. His research interests include evolutionary design, generative art, and data remapping for artistic performance and installation. Keith Ruston: He is a student at The Ohio State University where he has earned his B.S. in Computer Science & Engineering (2003) and is currently pursuing his M.S. His major field of study is graphics and animation. His career goals include working in the animated film industry and/or computer game development.     

SmartPeerCast: a Smart QoS driven P2P live streaming framework

The P2P swarm technologies have been shown to be very efficient for medium scale content distribution systems in the last few years, such as the file sharing and video-on-demand (VOD) applications. However it is still an open topic about how to deploy the P2P paradigm for the real time video broadcasting (RTVB) applications. The P2P RTVB application is different from the cache based P2P system because it has more stringent restrictions for startup time and packet loss ratio. In this paper, an adaptive media broadcasting P2P framework named SmartPeerCast which employs the media transrating service to control the quality of service (QoS), is proposed. SmartPeerCast achieves a network awareness, codec awareness, and high performance RTVB service with four key designs: (1) It groups the newly joined peers into different quality clusters by their uploading capability. This clustering mechanism avoids the bandwidth bottleneck between the heterogeneous peers of the overall P2P overlay by only forwarding the same quality stream over the peers in the same cluster. (2) The streaming quality is adjusted adaptively between the sending and the receiving peers by a Smart QoS algorithm to compensate for the network jitters to reduce the receiving peer’s playback jitter. (3) The receiving peer monitors the data forwarding QoS of the sending peer to select the best suitable parent node dynamically. The SmartPeerCast uses this Smart QoS framework to implement an incentive mechanism to award the peers with high uploading contributions by migrating them to a higher quality cluster. (4) A transrating engine is used at the leaf nodes of the high quality cluster to forward the stream with suitable bits rate to the nodes of the low quality cluster; this transrating service not only can fully utilize the uploading bandwidth of the peers in the higher quality cluster but also avoids the bandwidth bottleneck of stream forwarding between the heterogeneous peers. Our experiment results and the real deployment show that SmartPeerCast can eliminate the bandwidth bottleneck and content bottleneck between the heterogeneous peers with a smaller startup time and packet loss and it is a high performance and medium scale P2P RTVB framework.     

A suitable server placement for peer-to-peer live streaming

With the rapid growth of the scale, complexity, and heterogeneity of Peer-to-Peer (P2P) systems, it has become a great challenge to deal with the peer’s network-oblivious traffic and self-organization problems. A potential solution is to deploy servers in appropriate locations. However, due to the unique features and requirements of P2P systems, the traditional placement models cannot yield the desirable service performance. To fill this gap, we propose an efficient server placement model for P2P live streaming systems. Compared to the existing solutions, this model takes the Internet Service Provider (ISP) friendly problem into account and can reduce the cross-network traffic among ISPs. Specifically, we introduce the peers’ contribution into the proposed model, which makes it more suitable for P2P live streaming systems. Moreover, we deploy servers based on the theoretical solution subject to practical data and apply them to practical live streaming applications. The experimental results show that this new model can reduce the amount of cross-network traffic and improve the system efficiency, has a better adaptability to Internet environment, and is more suitable for P2P systems than the traditional placement models.     

A streaming edge sampling method for network visualization

Knowledge and Information Systems - Visualization strategies facilitate streaming network analysis by allowing its exploration through graphical and interactive layouts. Depending on the strategy...