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 task-level adaptive MapReduce framework for real-time streaming data in healthcare applications

Healthcare scientific applications, such as body area network, require of deploying hundreds of interconnected sensors to monitor the health status of a host. One of the biggest challenges is the streaming data collected by all those sensors, which needs to be processed in real time. Follow-up data analysis would normally involve moving the collected big data to a cloud data center for status reporting and record tracking purpose. Therefore, an efficient cloud platform with very elastic scaling capacity is needed to support such kind of real time streaming data applications. The current cloud platform either lacks of such a module to process streaming data, or scales in regard to coarse-grained compute nodes.In this paper, we propose a task-level adaptive MapReduce framework. This framework extends the generic MapReduce architecture by designing each Map and Reduce task as a consistent running loop daemon. The beauty of this new framework is the scaling capability being designed at the Map and Task level, rather than being scaled from the compute-node level. This strategy is capable of not only scaling up and down in real time, but also leading to effective use of compute resources in cloud data center. As a first step towards implementing this framework in real cloud, we developed a simulator that captures workload strength, and provisions the amount of Map and Reduce tasks just in need and in real time.To further enhance the framework, we applied two streaming data workload prediction methods, smoothing and Kalman filter, to estimate the unknown workload characteristics. We see 63.1% performance improvement by using the Kalman filter method to predict the workload. We also use real streaming data workload trace to test the framework. Experimental results show that this framework schedules the Map and Reduce tasks very efficiently, as the streaming data changes its arrival rate.     

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 spark-based big data analysis framework for real-time sentiment prediction on streaming data

There are many data sources that produce large volumes of data. The Big Data nature requires new distributed processing approaches to extract the valuable information. Real-time sentiment analysis is one of the most demanding research areas that requires powerful Big Data analytics tools such as Spark. Prior literature survey work has shown that, though there are many conventional sentiment analysis researches, there are only few works realizing sentiment analysis in real time. One major point that affects the quality of real-time sentiment analysis is the confidence of the generated data. In more clear terms, it is a valuable research question to determine whether the owner that generates sentiment is genuine or not. Since data generated by fake personalities may decrease accuracy of the outcome, a smart/intelligent service that can identify the source of data is one of the key points in the analysis. In this context, we include a fake account detection service to the proposed framework. Both sentiment analysis and fake account detection systems are trained and tested using Naïve Bayes model from Apache Spark's machine learning library. The developed system consists of four integrated software components, ie, (i) machine learning and streaming service for sentiment prediction, (ii) a Twitter streaming service to retrieve tweets, (iii) a Twitter fake account detection service to assess the owner of the retrieved tweet, and (iv) a real-time reporting and dashboard component to visualize the results of sentiment analysis. The sentiment classification performances of the system for offline and real-time modes are 86.77% and 80.93%, respectively.     

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.

PrivStream: A privacy-preserving inference framework on IoT streaming data at the edge

Edge computing combining with artificial intelligence (AI) has enabled the timely processing and analysis of streaming data produced by IoT intelligent applications. However, it causes privacy risk due to the data exchanges between local devices and untrusted edge servers. The powerful analytical capability of AI further exacerbates the risks because it can even infer private information from insensitive data. In this paper, we propose a privacy-preserving IoT streaming data analytical framework based on edge computing, called PrivStream, to prevent the untrusted edge server from making sensitive inferences from the IoT streaming data. It utilizes a well-designed deep learning model to filter the sensitive information and combines with differential privacy to protect against the untrusted edge server. The noise is also injected into the framework in the training phase to increase the robustness of PrivStream to differential privacy noise. Taking into account the dynamic and real-time characteristics of streaming data, we realize PrivStream with two types of models to process data segment with fixed length and variable length, respectively, and implement it on a distributed streaming platform to achieve real-time streaming data transmission. We theoretically prove that Privstream satisfies $\epsilon$-differential privacy and experimentally demonstrate that PrivStream has better performance than the state-of-the-art and has acceptable computation and storage overheads.     

博弈论框架下P2P实时流的合作模型研究

针对P2P实时流环境中出现的“搭便车”和“公共悲剧”问题,提出一种博弈论框架下的激励合作模型。分析该模型达到Nash均衡和Pareto最优状态下对应的比例公平策略优化。并考虑存在欺骗行为的情况,研究对应的节点行为策略。理论分析表明,该模型能够刺激节点合作,并且对节点的欺骗行为具有抑制作用。     

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.     

Online anomaly detection for multi‐source VMware using a distributed streaming framework

Anomaly detection refers to the identification of patterns in a dataset that do not conform to expected patterns. Such non‐conformant patterns typically correspond to samples of interest and are assigned to different labels in different domains, such as outliers, anomalies, exceptions, and malware. A daunting challenge is to detect anomalies in rapid voluminous streams of data. This paper presents a novel, generic real‐time distributed anomaly detection framework for multi‐source stream data. As a case study, we investigate anomaly detection for a multi‐source VMware‐based cloud data center, which maintains a large number of virtual machines (VMs). This framework continuously monitors VMware performance stream data related to CPU statistics (e.g., load and usage). It collects data simultaneously from all of the VMs connected to the network and notifies the resource manager to reschedule its CPU resources dynamically when it identifies any abnormal behavior from its collected data. A semi‐supervised clustering technique is used to build a model from benign training data only. During testing, if a data instance deviates significantly from the model, then it is flagged as an anomaly. Effective anomaly detection in this case demands a distributed framework with high throughput and low latency. Distributed streaming frameworks like Apache Storm, Apache Spark, S4, and others are designed for a lower data processing time and a higher throughput than standard centralized frameworks. We have experimentally compared the average processing latency of a tuple during clustering and prediction in both Spark and Storm and demonstrated that Spark processes a tuple much quicker than storm on average. Copyright © 2016 John Wiley & Sons, Ltd.     

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 scoping review of the use of lab streaming layer framework in virtual and augmented reality research

Virtual Reality - The use of multimodal data allows excellent opportunities for human–computer interaction research and novel techniques regarding virtual and augmented reality (VR/AR)...