Reverse proxy, as one of the important components required by the data center to provide application services, has functions of access control, load balancing, connecting to different types of networks, etc. In the future, as the application services requiring reverse proxy further increase, network types become more and more diverse and complex, and the network hierarchy becomes higher and higher, reverse proxy will change from a single layer to multiple layers to form a reverse proxy chain. The construction of the reverse proxy chain will become one of the bottlenecks of data center networking operation and maintenance. In this paper, we propose a method of automatically constructing reverse proxy chains to avoid the problem of manual static configuration of the reverse proxy chain which is time-consuming, laborious, and difficult to maintain. In our software-defined networking experiment, we simulated a full-binary-tree-like topology of 1534 nodes. We recorded the time to generate and remove proxy chains of various lengths. The average time to generate all reverse proxy chains in the topology consisting of 1534 nodes with 100ms delay is around 5100ms, much smaller than manual configuration, which usually needs several hours.

Successful software maintenance is becoming increasingly critical due to the increasing dependence of our society and economy on software systems. One key problem of software maintenance is the difficulty in understanding the evolving software systems. Program workflows can help system operators and administrators to understand system behaviors and verify system executions so as to greatly facilitate system maintenance. In this paper, we propose an algorithm to automatically discover program workflows from event traces that record system events during system execution. Different from existing workflow mining algorithms, our approach can construct concurrent workflows from traces of interleaved events. Our workflow mining approach is a three-step coarse-to-fine algorithm. At first, we mine temporal dependencies for each pair of events. Then, based on the mined pair-wise tem-poral dependencies, we construct a basic workflow model by a breadth-first path pruning algorithm. After that, we refine the workflow by verifying it with all training event traces. The re-finement algorithm tries to find out a workflow that can interpret all event traces with minimal state transitions and threads. The results of both simulation data and real program data show that our algorithm is highly effective.

In a peer-to-peer (P2P) live streaming system, the streaming quality of an end user is much affected by the aggregate download bandwidth from the partners. In this paper, we propose a stochastic model for the P2P streaming system to analyze the asymptotic probability distribution of the aggregate download bandwidth of a peer. Using renewal theory, we derive a close form expression for the asymptotic mean and variance of the aggregate download bandwidth. We analyze the relationship between the distribution of the aggregate download bandwidth and peer dynamics, bandwidth heterogeneity as well as algorithm design. We show that reducing the partner search time and increasing the peer degree can bring better performance. We also analyze the effect of the local optimization procedure that is adopted by most P2P streaming systems. We find that local optimization can only redistribute the bandwidth resources among peers with different life time. We validate our findings through extensive simulations and the results show that our model can describe the bandwidth distribution with good accuracy.

Detection of execution anomalies is very important for the maintenance, development, and performance refinement of large scale distributed systems. Execution anomalies include both work flow errors and low performance problems. People often use system logs produced by distributed systems for troubleshooting and problem diagnosis. However, manually inspecting system logs to detect anomalies is unfeasible due to the increasing scale and complexity of distributed systems. Therefore, there is a great demand for automatic anomalies detection techniques based on log analysis. In this paper, we propose an unstructured log analysis technique for anomalies detection. In the technique, we propose a novel algorithm to convert free form text messages in log files to log keys without heavily relying on application specific knowledge. The log keys correspond to the log-print statements in the source code which can provide cues of system execution behavior. After converting log messages to log keys, we learn a Finite State Automaton (FSA) from training log sequences to present the normal work flow for each system component. At the same time, a performance measurement model is learned to characterize the normal execution performance based on the log messages' timing information. With these learned models, we can automatically detect anomalies in newly input log files. Experiments on Hadoop and SILK show that the technique can effectively detect running anomalies.

Learning the underlying model from distributed data is often useful for many distributed systems. In this paper, we study the problem of learning a non-parametric model from distributed observations. We propose a gossip-based distributed kernel density estimation algorithm and analyze the convergence and consistency of the estimation process. Furthermore, we extend our algorithm to distributed systems under communication and storage constraints by introducing a fast and efficient data reduction algorithm. Experiments show that our algorithm can estimate underlying density distribution accurately and robustly with only small communication and storage overhead.

Effectively coding multiview visual content is an indispensable research topic because multiview image and video that provide greatly enhanced viewing experiences often contain huge amounts of data. Generally, conventional hybrid predictive-coding methodologies are adopted to address the compression by exploiting the temporal and interviewpoint redundancy existing in a multiview image or video sequences. However, their key yet time-consuming component, motion estimation (ME), is usually not efficient in interviewpoint prediction or disparity estimation (DE), because interviewpoint disparity is completely different from temporal motion existing in the conventional video. Targeting a generic fast DE framework for interviewpoint prediction, we propose a novel DE technique in this paper to accelerate the disparity search by employing epipolar geometry. Theoretical analysis, optimal disparity vector distribution histograms, and experimental results show that the proposed epipolar geometry-based DE can greatly reduce search region and effectively track large and irregular disparity, which is typical in convergent multiview camera setups. Compared with the existing state-of-the-art fast ME approaches, our proposed DE can obtain a similar coding efficiency while achieving a significant speedup for interviewpoint prediction and coding. Moreover, a robustness study shows that the proposed DE algorithm is insensitive to the epipolar geometry estimation noise. Hence, its wide application for multiview image and video coding is promising

Many existing multiview image/video coding techniques remove inter-viewpoint redundancy by applying disparity compensation in a conventional video coding framework, e.g., H.264/MPEG-4 AVC. However, conventional methodology works ineffectively as it ignores the special characteristics of inter-viewpoint disparity. In this paper, we propose a geometric prediction methodology for accurate disparity vector (DV) prediction, such that we can largely reduce the disparity compensation cost. Based on the new DV predictor, we design a basic framework that can be implemented in most existing multiview image/video coding schemes. We also use state-of-the-art H.264/MPEG-4 AVC as an example to illustrate how the proposed framework can be integrated with conventional video coding algorithms. Our experiments show proposed scheme can effectively tracks disparity and greatly improves coding performance. Compared with H.264/MPEG-4 AVC codec, our scheme outperforms maximally 1.5 dB when encoding some typical multiview image sequences. We also carry out an experiment to evaluate the robustness of our algorithm. The results indicate our method is robust and can be used in practical applications.

Increased speeds of PCs and networks have made media communications possible on the Internet. Today, the need for desktop videoconferencing is experiencing robust growth in both business and consumer markets. However, the synchronous delivery of high-volume media content is still a big challenge under a current heterogeneous Internet environment. In this paper, we present a multiparty videoconferencing system based on a peer-to-peer (P2P) solution. The contribution of our paper is twofold. On the one hand, we design an application-level multicast scheme which intends to tolerate the heterogeneity in videoconferencing applications. Design tradeoffs are analyzed and our decisions are made based on extensive experimentation. On the other, we design a five-layer architecture for implementing a multiparty videoconferencing system. This architecture makes a clear-cut distinction between different functional modules and therefore provides rich flexibility in feature adaptation. We believe that our work can be a helpful reference in other efforts on building desktop videoconferencing systems.

Available bandwidth measurement is essential to applications running on the best-effort Internet. In this paper, we propose an available bandwidth measurement technique named MoSeab. The idea behind MoSeab is a direct probing technique based on a statistical model for network data transmissions. Differing from the other direct probing techniques, MoSeab does not require any a priori knowledge of network path. It has proven valid even when there are multiple bottlenecks. Simulation and real Internet experiments demonstrate the accuracy and robustness of MoSeab, and show its advantages over Spruce, PathChirp, and IGI. Moreover, its probing traffic control mechanism makes MoSeab a non-intrusive solution suitable to be integrated into various network applications.

To efficiently encode data-intensive multi-view imaging content, conventional hybrid predictive coding methodologies choose to address the compression by exploiting temporal and inter-viewpoint redundancy. However, their key yet time-consuming component, motion estimation (ME), is usually not efficient in inter-viewpoint prediction because inter-viewpoint motion is quite different from temporal motion. In essence, inter-viewpoint correlation is subject to epipolar geometry, which provides constraints for multi-view image sequences. A fast inter-viewpoint ME technique is hence proposed in this paper to accelerate the encoding by employing epipolar geometry. Theoretical analysis and experimental results prove that the proposed ME algorithm can greatly reduce search region and effectively track large and irregular motion that is typical for convergent multi-view camera setups. As a result, compared with fast full search at large search size adopted in H.264, our proposed ME algorithm can obtain a similar coding efficiency while achieving a speedup ratio of 2.9.

Inter-color correlation between the luminance component and chrominance components has been utilized for color image coding for years. However, the correlation has not been clearly analyzed. In this paper, we analyze the inter-color correlation and answer two questions related to color image coding: (1) what kind of inter-color correlation exists in color images after the discrete wavelet transform?; and, (2) how strong is it? This analysis helps us to find a most suitable inter-color context and eventually leads to a new embedded color image codec. By using the discovered inter-color context, significant performance improvement can be achieved when encoding chrominance components.

As a recently emerging service, multi-view video provides a new viewing experience with high degree of freedom. However, due to the huge data amounts transferred, multi-view video's delivery remains a daunting challenge. In this paper, we propose a multi-view video-streaming system based on IP multicast. It can support a large number of users while still keeping a high degree of interactivity and low bandwidth consumption. Based on a careful user study, we have developed two schemes: one is for automatic delivery and the other for on-demand delivery. In automatic delivery, a server periodically multicasts special effect snapshots at a certain time interval. In on-demand delivery, the server delivers the snapshots based on distribution of users' requests. We conducted extensive experiments and user-experience studies to evaluate the proposed system's performance, and found that the system could provide satisfying multi-view video service for users on a large scale

In an H.264 video encoder, motion estimation (ME) is the most time-consuming component. The ME process consists of two stages, integer pixel search and fractional pixel search. Since the complexity of integer pixel search has been greatly reduced by numerous fast ME algorithms, the computation overhead required by fractional pixel ME has become relatively significant. To reduce the complexity of fractional pixel ME, we propose a prediction-based directional fractional pixel ME algorithm. We utilize more accurate motion vector predictions and directional search to achieve better computation reduction. We further propose an early termination method to decrease the amount of search. Experimental results show that, compared to the full search sub-pel ME and the fast sub-pel ME proposed in H.264, the proposed method can reduce up to 84% and 74% of fractional pixel search points respectively, with a negligible degradation in quality.

As a key part of universal source coding, context quantization is very important for improving compression performance. However, in most existing methods, the quantizer is trained offline and is fixed due to the complexity of finding a good quantizer and the significant overhead of representing the quantizer. This paper proposes a novel online context quantization approach that achieves high coding efficiency with low quantizer overhead and computational complexity. It first partitions the context into groups according to the number of significant context events. A layer-based context quantization is then applied on these groups. The proposed method is applied for embedded wavelet image coding. Compared with the JPEG2000 coder, up to 0.6 dB improvements can be achieved on the standard 512 /spl times/ 512 test images. And more improvements are observed on images at lower resolutions.

With the rapid development of digital technology in consumer electronics, the demand to preserve raw image data for further editing or repeated compression is increasing. Traditional lossless image coders usually consist of computationally intensive modeling and entropy coding phases, therefore might not be suitable to mobile devices or scenarios with a strict real-time requirement. This paper presents a new image coding algorithm based on a simple architecture that is easy to model and encode the residual samples. In the proposed algorithm, each residual sample is separated into three parts: (1) a sign value, (2) a magnitude value, and (3) a magnitude level. A tree structure is then used to organize the magnitude levels. By simply coding the tree and the other two parts without any complicated modeling and entropy coding, good performance can be achieved with very low computational cost in the binary-uncoded mode. Moreover, with the aid of context-based arithmetic coding, the magnitude values are further compressed in the arithmetic-coded mode. This gives close performance to JPEG-LS and JPEG2000.

With the rapid development of electronic and computing technology, multi-view video is attracting extensive interest recently due to its greatly enhanced viewing experience. In this paper, we present the system architecture for real-time capturing, processing, and interactive delivery of multi-view video. Unlike previous systems that mainly focus on multi-view video capturing, our system is designed to provide multi-view video service with high degree of interactivity in real time, which is still challenging in the current state of the technology. The proposed architecture tackles many practical problems in system calibration, object tracking, video compression, interactive delivery, etc. With the proposed system, users can interactively select their desired viewing directions and enjoy many exciting visual experiences, such as view switching, frozen moment and view sweeping, in real-time and with great freedom.

The H.264 video coding standard provides considerably higher coding efficiency than previous standards do, whereas its complexity is significantly increased at the same time. In an H.264 encoder, the most time-consuming component is variable block-size motion estimation. To reduce the complexity of motion estimation, an early termination algorithm is proposed in this paper. It predicts the best motion vector by examining only one search point. With the proposed method, some of the motion searches can be stopped early, and then a large number of search points can be skipped. The proposed method can work with any fast motion estimation algorithm. Experiments are carried out with a fast motion estimation algorithm that has been adopted by H.264. Results show that significant complexity reduction is achieved while the degradation in video quality is negligible.

The rapid development of wireless networks and mobile devices has made mobile video communication a particularly promising service. We previously proposed an effective video form, scalable portrait video. In low-bandwidth conditions, portrait video possesses clearer shape, smoother motion, and much cheaper computational cost than discrete cosine transform (DCT)-based schemes. However, the bit rate of portrait video cannot be accurately modeled by a rate-distortion function as in DCT-based schemes. How to effectively control the bit rate is a hard challenge for portrait video. In this paper, we propose a novel model-based rate-control method. Although the coding parameters cannot be directly calculated from the target bit rate, we build a model between the bit-rate reduction and the percentage of less probable symbols (LPS) based on the principle of entropy coding, which is referred to as the LPS-rate model. We use this model to obtain the desired coding parameters. Experimental results show that the proposed method not only effectively controls the bit rate, but also significantly reduces the number of skipped frames. The principle of this method can also be applied to general bit plane coding in other image processing and video compression technologies.

The increasing demand for multi-party videoconferencing has aroused the research interest in the underlying multicast support. In this paper, we propose DigiMetro, an application-level multicast system tailored to small and impromptu videoconferencing. Breaking through the conventional wisdom to use shared overlay to handle multiple data sources, DigiMetro organizes the data delivery routes as source-specific trees, which are first constructed by a local greedy algorithm and then gradually improved by a global refinement procedure. Extensive simulation experiments demonstrate the efficiency of both algorithms. Moreover, DigiMetro is able to handle different video bit rates and provide different services over voice/video streams.

In videoconferencing, the image quality is significantly affected by the illumination condition. Unsatisfactory illumination conditions may lead to underexposure or overexposure of the area of interest, in particular a human face. To resolve this issue, we propose a solution to improve image quality automatically by correcting exposure and enhancing contrast. Our work is characterized by a method for automatically building a skin-color model and a novel contrast enhancement approach. Some techniques that can reduce the computational cost are also introduced. Experimental results show that obvious improvement in image quality is achieved while the computation overhead is very small. The proposed solution can be integrated into videoconferencing systems and is especially suitable for scenarios where low-complexity computing is required.

The increased speeds of PCs and networks have made media communication possible on the Internet. However, nearly ten years after the first release of Microsoft NetMeeting, Internet video telephony is still limited to the point-to-point communication mode. Today, people have a need for an easy-to-use multi-party video conferencing tool that can connect families and friends around the world over the Internet. We present DigiParty, a fully distributed multi-party video conferencing system. DigiParty employs a full mesh conferencing architecture and adopts a loosely coupled conferencing mode. A novel conference control protocol is designed with the system. DigiParty can be integrated with any existing instant messaging services and is applicable to all types of Internet connections.

Real-time software-based video codec is widely used on PCs with relatively strong computing capability. However, mobile devices, such as pocket PCs and handheld PCs, still suffer from weak computational power, short battery lifetime and limited display capability. We developed a practical low-complexity real-time video codec for mobile devices. Several methods that can significantly reduce the computational cost are adopted in this codec and described in this paper, including a predictive algorithm for motion estimation, the integer discrete cosine transform (IntDCT), and a DCT/quantizer bypass technique. A real-time video communication implementation of the proposed coded is also introduced. Experiments show that substantial computation reduction is achieved while the loss in video quality is negligible. The proposed codec is very suitable for scenarios where low-complexity computing is required.

Wireless networks have been rapidly developing in recent years. General Packet Radio Service (GPRS) and Code Division Multiple Access (CDMA 1X) for wide areas, and 802.11 and Bluetooth for local areas have already emerged. Broadband wireless networks urgently call for rich contents for consumers. Among various possible applications, video communication is one of the most promising for mobile devices on wireless networks. This paper describes the generation, coding, and transmission of an effective video form, scalable portrait video for mobile video communication. As an expansion to bilevel video, portrait video is composed of more gray levels, and therefore possesses higher visual quality while it maintains a low bit rate and low computational costs. Portrait video is a scalable video in that each video with a higher level always contains all the information of the video with a lower level. The bandwidths of 2-4-level portrait videos fit into the bandwidth range of 20-40 kbps that GPRS and CDMA 1X can stably provide; therefore, portrait video is very promising for video broadcast and communication on 2.5-G wireless networks. With portrait video technology, we are the first to enable two-way video communication on pocket PCs and handheld PCs.

The rapid development of wired and wireless networks tremendouslyfacilitates communications between people. However, most of thecurrent wireless networks still work in low bandwidths, and mobiledevices still suffer from weak computational power, short batterylifetime and limited display capability. We developed a very lowbit-rate bi-level video coding technique, which can be used invideo communications almost anywhere, anytime on any device. Thespirit of this method is that rather than giving highest priorityto the basic colors of an image as in conventional DCT-basedcompression methods, we give preference to the outline features ofscenes when we have limited bandwidths. These features can berepresented by bi-level image sequences that are converted fromgray-scale image sequences. By analyzing the temporal correlationbetween successive frames and flexibilities in the scenepresentation using bi-level images, we achieve very high ratioswith our bi-level video compression scheme. Experiments show thatin low bandwidths, our method provides clearer shape, smoothermotion, shorter initial latency and much cheaper computational costthan do DCT-based methods. Our method is especially suitable forsmall mobile devices such as handheld PCs, palm-size PCs and mobilephones that possess small display screens and light computationalpower, and work in low bandwidth wireless networks. We have builtPC and Pocket PC versions of bi-level video phone systems, whichtypically provide QCIF-size video with a frame rate of 5-15 fps fora 9.6 Kbps bandwidth.

As the Internet and wirless networks are developed rapidly, the demand of communicating anywhere, anytime on any device emerges. However, most of the current wireless networks still work in low bandwidths, and mobile devices still suffer from weak computational power, short battery lifetime and limited display capability. We developed portrait video phone systems that can run on Pcs and Pocket Pcs at very low bit rates through the Internet. The core technology that portrait video phones employ is the so-called portrait video (or bi-level video) codec. Portrait video codec first converts a full-color video into a black/white image sequence and then compresses it into a black/white portrait-like video. Portrait video processes clearer shape, smoother motion, shorter initial latency, and cheaper computational cost than MPEG2, MPEG4 and H.263 for low bandwidths. Typically the portrait video phone provides QCIF-size video with a frame rate of 5-15 fps for a 9.6 Kbps video bandwidth. The portrait video is so small that it can even be transmitted through an HTTP proxy as text. Experiments show that the portrait video phones work well on ordinary GSM wireless telecommunication networks.