15261618
A Jobs Allocation Strategy for Multiple DRBL Diskless Linux Clusters with Condor Schedulers
In this paper, we construct four DRBL-based diskless Linux PC clusters and install Globus Toolkit and Condor scheduler on the master node of each cluster. All diskless clusters can be merged into a powerful computing environment. To utilize the computing power, we propose a jobs allocation strategy for application execution. We use HPL to conduct some performance evaluations and analyze those data to form a formula for jobs allocation. The information server will calculate the value of computing power based on our formula and find the most appropriate cluster to execute the job. It can make our large computing environment that can execute the jobs in a more efficient way
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.
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.
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.
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.
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.
15205794
Specification of Multi-Agent Systems in the Gamma Language
The suitability of using chemical reaction metaphor (CRM) to model multi-agent systems (MASs) is justified by CRM's capacity in specifying dynamic features of multi-agent systems. This paper presents a case study that demonstrates the applications of CRM in specifying multi-agent systems. The case study is the specification of a navigation training system. Given the dynamic and concurrent nature of multi-agent systems, we find that the chemical reaction metaphor provides a mechanism for describing the overall architecture of the distributed multi-agent systems precisely and concisely, while giving the design of the real system a solid starting point and allowing step-by-step refinement of the system using transformational methods
15244741
Automated Case Base Creation and Management
In this paper, we report on a scheme for automated case base creation and management. The scheme aims at reducing the difficulty and human effort required for case creation. This paper provides an overview of the proposed scheme and outlines its technical implementation as an automated case creation system for the Integrated Diagnostic System. Some experimental results for testing the scheme and an interactive tool for evaluating the constructed case base are presented.
15657957
Automatic Summarization of Chinese and English
As a result of the rapid growth in Internet access, significantly more information has become available online in real time. However, there is not sufficient time for users to read large volumes of information and make decisions accordingly. The problem of information-overloading can be resolved through the application of automatic summarization. Many summarization systems for documents in different languages have been implemented. However, the performance of summarization system on documents in different languages has not yet been investigated. In this paper, we compare the result of fractal summarization technique on parallel documents in Chinese and English. The grammatical and lexical differences between Chinese and English have significant effect on the summarization processes. Their impact on the performances of the summarization for the Chinese and English parallel documents is compared.
46023466
Finding Interesting Associations without Support Pruning
Association-rule mining has heretofore relied on the condition of high support to do its work efficiently. In particular, the well-known a priori algorithm is only effective when the only rules of interest are relationships that occur very frequently. However, there are a number of applications, such as data mining, identification of similar Web documents, clustering, and collaborative filtering, where the rules of interest have comparatively few instances in the data. In these cases, we must look for highly correlated items, or possibly even causal relationships between infrequent items. We develop a family of algorithms for solving this problem, employing a combination of random sampling and hashing techniques. We provide analysis of the algorithms developed and conduct experiments on real and synthetic data to obtain a comparative performance analysis.
2577694
Efficient acoustic index for music retrieval with various degrees of similarity
Content-based music retrieval research has mostly focused on symbolic data rather than acoustical data. Given that there are no general-purpose transcription algorithms that can convert acoustical data into musical scores, new methods are needed to do music retrieval on acoustical data. In this paper, we review some existing methods on contentbased music retrieval, discuss different definitions of music similarity, and present a new framework to perform music indexing and retrieval. The framework is based on an earlier prototype we developed, with significant improvements.In our framework known as MACSIS, each audio file is broken down into small segments and converted into feature vectors. All vectors are stored in a high-dimensional indexing structure called LSH, a probabilistic indexing scheme that makes use of multiple hashing instances in parallel. At retrieval time, small segments of audio matches are retrieved from the index and pieced together using the Hough Transform technique, and results are used as the basis to rank candidate matches.