46475571
A Chronological History-Based Execution Time Estimation Model for Embarrassingly Parallel Applications on Grids
In order to identify and schedule jobs that are suitable for determined resources, an execution time estimation model is required. In this paper, it is described a Chronological history-based execution time estimation model to predict current execution time, according to the previous execution results. We built a heterogeneous computational Grid environment using Globus Toolkit, and our research is focused in Grid computing environments and to execute parallel jobs on multiple resources by measuring its accuracy. The experimental results shown that our model can accurately predict the execution time of embarrassingly parallel applications.
32464432
An Enhanced Parallel Loop Self-Scheduling Scheme for Cluster Environments
In this paper, a parallel loop self-scheduling scheme for heterogeneous PC cluster systems is proposed. Though the proposed scheme does allow users to choose parameters before the execution initialization phase, there are still weaknesses that motivate us to go further with new improvements in that scheme. For instance, a decision on a fixed and monotonous parameter can easily lead to invalid schedule by using previous input information. Thus, it is proposed in this paper a new scheme, where the scheduling parameter can be adjusted dynamically and fit into most widely available computer systems, in order to provide higher overall performance.
7849532
A Peer-to-Peer Resource Sharing System with Data Grid Technology for Mobile Devices
Increases in mobile device processing power, storage capacity, and connectivity in recent years have led to a rapid rise in mobile device use. But mobile users face challenges in making connections with other users in order to share resources. To address this issue, we propose an architecture that uses peer-to-peer protocol to establish a resource sharing platform for mobile devices, combined with a data grid to help analyze and classify resources.
5672930
An e-Learning Platform Based on Grid Architecture
The overall popularity of the Internet has helped e-learning become a hot method for learning in recent years. Over the Internet, learners can freely absorb new knowledge without restrictions on time or place. Many companies have adopted e-learning to train their employees. An e-learning system can make an enterprise more competitive by increasing the knowledge of its employees. E-learning has been shown to have impressive potential in e-commerce. At present, most e-learning environment architectures use single computers or servers as their structural foundations. As soon as their work loads increase, their software and hardware must be updated or renewed. This is a big burden on organizations that lack sufficient funds. Thus, in this study we employ a kind of Grid Computing technology, called the ”Data Grid” to integrate idle computer resources in enterprises into e-learning platforms, thus eliminating the need to purchase costly high-level servers and other equipment.
12951016
A shareable e-learning platform using data grid technology
Over the past decade, speed in the transmission of knowledge is no longer the way it used to be. Due to this influence, conventional educational methodology has also followed this trend and has gone through a momentous transformation. An excellent example is e-learning which has achieved and broken through the restrictions of time and space. In recent years it has become one of the most popular teaching methods. SCORM (Sharable Course Object Reference Model) the most popular e-learning standard has been adapted from many differing e-learning platforms. Normally, courseware meeting SCORM standards is sharable with other e-learning platforms. Most e-learning platforms however are unable to share courseware directly on the Internet. With this in mind, we have employed one type of grid computing technology called "data grid" in order to achieve this. This technology allows the integration of different SCORM platforms into a huge e-learning environment.
7749374
A Performance-Based Approach to Dynamic Workload Distribution for Master-Slave Applications on Grid Environments
Effective workload distribution techniques can significantly reduce the total completion time of a program on grid computing environments. In this paper, we propose a dynamic performance-based workload partition approach for master-slave types of applications on grids. Furthermore, we implement two types of applications and conduct the experimentations on our grid testbed. Experimental results showed that our method could execute more efficiently than traditional schemes.
6374629
A Multimedia Synchronization Model and Its Implementation in Transport Protocols
An implementation of a synchronization mechanism in transport protocol to support multimedia applications over a packet or cell switched network is proposed. In designing such a mechanism for practical use, ease of implementation and capability of handling random delay of packets are two key issues for success. Since the random delay of packet or cell switched networks makes synchronization among media more complicated after the transmission across the network, a model which considers the random transmission delay is hence required to specify the temporal relationship among media. Therefore, a real-time synchronization model is presented to satisfy this requirement in the paper. Based on the proposed synchronization model, a transport protocol, namely the multimedia synchronization transport protocol (MSTP), is designed and implemented. We have implemented a prototype system using the MSTP protocol and built a simulation system for the wide area network (WAN) environment in order to evaluate the performance of the MSTP protocol.
7211602
Improved fast gauss transform and efficient kernel density estimation
Evaluating sums of multivariate Gaussians is a common computational task in computer vision and pattern recognition, including in the general and powerful kernel density estimation technique. The quadratic computational complexity of the summation is a significant barrier to the scalability of this algorithm to practical applications. The fast Gauss transform (FGT) has successfully accelerated the kernel density estimation to linear running time for low-dimensional problems. Unfortunately, the cost of a direct extension of the FGT to higher-dimensional problems grows exponentially with dimension, making it impractical for dimensions above 3. We develop an improved fast Gauss transform to efficiently estimate sums of Gaussians in higher dimensions, where a new multivariate expansion scheme and an adaptive space subdivision technique dramatically improve the performance. The improved FGT has been applied to the mean shift algorithm achieving linear computational complexity. Experimental results demonstrate the efficiency and effectiveness of our algorithm.
35542914
A Decoupled Architecture for Application-Specific File Prefetching
19017390
Amperometric Readout and Electrode Array Chip for Bioelectrochemical Sensors
As nanostructured bioelectronic interfaces continue to evolve for sensor applications, new readout circuits are needed to harness their capabilities. This paper presents a single-chip amperometric readout circuit and electrode array system suitable for bioelectrochemical measurements. The chip features a CMOS potentiostat with high resolution, range-programmable current readout and electrochemical cell potential drive circuitry, which can perform on-chip chronoamperometry and cyclic voltammetry assays. Through post-CMOS fabrication, the surface of the chip is prepared with an array of electrodes suitable for formation of bioelectronic interfaces and on-chip bioelectrochemical measurements. The 3times3mm2 chip nominally hosts a 4times4 working electrode array and supports amperometric outputs ranging from 10pA to 10muA with sub-pA resolution.
12121845
Automatic image orientation detection
We present an algorithm for automatic image orientation estimation using a Bayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a learning vector quantizer (LVQ) can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how principal component analysis (PCA) and linear discriminant analysis (LDA) can be used as a feature extraction mechanism to remove redundancies in the high-dimensional feature vectors used for classification. The proposed method is compared with four different commonly used classifiers, namely k-nearest neighbor, support vector machine (SVM), a mixture of Gaussians, and hierarchical discriminating regression (HDR) tree. Experiments on a database of 16 344 images have shown that our proposed algorithm achieves an accuracy of approximately 98% on the training set and over 97% on an independent test set. A slight improvement in classification accuracy is achieved by employing classifier combination techniques.