3730862
Distributed Resource Allocation in Stream Processing Systems
Distributed stream processing architecture has emerged as appealing solution to coping with the analysis of large amount of data from dispersed sources. A fundamental problem in such stream processing systems is how to best utilize the available resources so that the overall system performance is optimized. We consider a distributed stream processing system that consists of a network of cooperating servers, collectively providing processing services for multiple data streams. Each stream is required to complete a series of operations on various servers. We assume all servers have finite computing resources and all communication links have finite available bandwidth. The problem is to find distributed schemes to allocate the limited computing resources as well as the communication bandwidth in the system so as to achieve a maximum concurrent throughput for all output streams. We present a generalized multicommodity flow model for the above problem. We develop a distributed resource allocation algorithm that guarantees the optimality. We also provide detailed analysis on the complexity of the algorithm and demonstrate the performance using numerical experiments.
3761715
A multicommodity flow model for distributed stream processing
The rapid development of computer technology has enabled streaming applications to emerge from many areas of the IT industry. A fundamental problem in such stream processing systems is how to best utilize the available resources so that the overall system performance is optimized. In most real time systems, applications are often running in a decentralized, distributed environment. At any given time, no server has the global information about all the servers in the system. It is thus desirable to have distributed solutions capable of adapting to or absorbing local changes in production rates and in network conditions. This paper addresses the fundamental resource allocation question in the distributed stream processing setting. Specifically, we consider a generic model for stream processing systems. Each stream is required to complete a series of tasks to become finished product. Each server is responsible for a subset of the processing tasks for possibly a subset of the streams. All servers have finite computing resources and all communication links have finite available bandwidth. We consider the resource allocation problem so as to maximize the total throughput of the system. Our problem can be described as a generalization of the traditional multicommodity flow problem [1]. Traditional multicommodity flow problem looks for the best way to use of the link capacities to deliver the maximum throughput for the flows in the whole network. In our problem, in addition to the link bandwidth constraints, we also have processing power constraints for each server. Furthermore, we allow flow shrinkage or expansion so as to model operations such
7762229
Edge detection by scale multiplication in wavelet domain
This paper proposes a wavelet based edge detection scheme by scale multiplication. The dyadic wavelet transforms at two adjacent scales are multiplied as a product function to magnify the edge structures and suppress the noise. Unlike many multiscale techniques that first form the edge maps at several scales and then synthesize them together, we determined the edges as the local maxima directly in the scale product after an efficient thrsholding. It is shown that the scale multiplication achieves better results than either of the two scales, especially on the localization performance. The dislocation of neighboring edges is also improved when the width of detection filter is set large to smooth noise. Experiments on natural images are compared with the Laplacian of Gaussian and Canny edge detection algorithms.
12034638
A block-based inter-band lossless hyperspectral image compressor
We propose a hyperspectral image compressor called BH which considers its input image as being partitioned into square blocks, each lying entirely within a particular band, and compresses one such block at a time by using the following steps: first predict the block from the corresponding block in the previous band, then select a predesigned code based on the prediction errors, and finally encode the predictor coefficient and errors. Apart from giving good compression rates and being fast, BH can provide random access to spatial locations in the image. We hypothesize that BH works well because it accommodates the rapidly changing image brightness that often occurs in hyperspectral images. We also propose an intra-band compressor called LM which is worse than BH, but whose performance helps explain BH's performance.
13978949
An experimental study of two graph analysis based component capture methods for object-oriented systems
The problem of how to partition a software system and thus capture its overall architecture and its constituent components has become a research focus in the community of software engineering. In the literature, many methods have been proposed for solving this problem. For example, both top-down and bottom-up methods based on analyzing the graph representation of software systems have been proposed. We report an experimental study of a top-down method and a bottom-up method. In our study, we focus on the capability of component capture, the capability of architecture recovery and the time complexity for the two methods. According to our results on two real world systems, the studied bottom-up method is superior to the studied top-down method in both aspects, although the time complexity of the bottom-up method remains a big concern for large systems.
7255987
Relationship between support vector set and kernel functions in SVM
Based on a constructive learning approach, covering algorithms, we investigate the relationship between support vector sets and kernel functions in support vector machines (SVM). An interesting result is obtained. That is, in the linearly non-separable case, any sample of a given sample setK can become a support vector under a certain kernel function. The result shows that when the sample setK is linearly non-separable, although the chosen kernel function satisfies Mercer’s condition its corresponding support vector set is not necessarily the subset ofK that plays a crucial role in classifyingK. For a given sample set, what is the subset that plays the crucial role in classification? In order to explore the problem, a new concept, boundary or boundary points, is defined and its properties are discussed. Given a sample setK, we show that the decision functions for classifying the boundary points ofK are the same as that for classifying theK itself. And the boundary points ofK only depend onK and the structure of the space at whichK is located and independent of the chosen approach for finding the boundary. Therefore, the boundary point set may become the subset ofK that plays a crucial role in classification. These results are of importance to understand the principle of the support vector machine (SVM) and to develop new learning algorithms.
53235192
Spacetime faces: high resolution capture for modeling and animation
We present an end-to-end system that goes from video sequences to high resolution, editable, dynamically controllable face models. The capture system employs synchronized video cameras and structured light projectors to record videos of a moving face from multiple viewpoints. A novel spacetime stereo algorithm is introduced to compute depth maps accurately and overcome over-fitting deficiencies in prior work. A new template fitting and tracking procedure fills in missing data and yields point correspondence across the entire sequence without using markers. We demonstrate a data-driven, interactive method for inverse kinematics that draws on the large set of fitted templates and allows for posing new expressions by dragging surface points directly. Finally, we describe new tools that model the dynamics in the input sequence to enable new animations, created via key-framing or texture-synthesis techniques.