Grid technology is the great progress of network after Internet, since grid enables the integrated and collaborative use of distributed computing resources owned and managed by multiple organizations, available over a...
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Grid technology is the great progress of network after Internet, since grid enables the integrated and collaborative use of distributed computing resources owned and managed by multiple organizations, available over a local or wide area network. In this paper, we provided a data grid platform with integrated storage to solve such data grid problems by using PVFS2 to increase the data storage space. The experimental results presented show the effectiveness of such proposed combination of technologies
In this paper, an efficient technique is proposed to optimize the geometie and arrangements of the slots of the CARLSAs. Moment method in carried out to obtain the unknown excitation coefficients after spliting the sl...
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Cluster and grid computing is a relatively new interdisciplinary field, where computerscience, engineering and computational biology as its core supporting disciplines. The rise of cluster and grid computing discipli...
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Electric Vehicles (EVs) are becoming increasingly popular in recent years. Battery Electric Vehicles (BEVs) are a type of EVs use stored energy more efficiently than typical internal combustion engine powered counterp...
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In order to solve real-time control problems, a good software design together with an appropriate control scheme and a system identification method are extremely importance. To facilitate the software design to cope w...
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In order to solve real-time control problems, a good software design together with an appropriate control scheme and a system identification method are extremely importance. To facilitate the software design to cope with such a time critical system, the concept of imprecise and approximate computation has been imposed and applied in real-time scheduling problems for more than a decade. Applying neural network to solve real-time problem is always a problem to neural network practitioners. In this paper, a principle for neural computation and real-time system 一 imprecise neural computation 一 will be presented. This principle extends the idea of imprecise computation in real-time systems by introducing concepts like mandatory neural structure and imprecise pruning. Using such concepts, it is able to design and analyze a real-time neural system for different real-time applications.
With the development of emerging social networks, such as Facebook and MySpace, security and privacy threats arising from social network analysis bring a risk of disclosure of confidential knowledge when the social ne...
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ISBN:
(纸本)9781615671090
With the development of emerging social networks, such as Facebook and MySpace, security and privacy threats arising from social network analysis bring a risk of disclosure of confidential knowledge when the social network data is shared or made public. In addition to the current social network anonymity de-identification techniques, we study a situation, such as in a business transaction network, in which weights are attached to network edges that are considered to be confidential (e.g., transactions). We consider perturbing the weights of some edges to preserve data privacy when the network is published, while retaining the shortest path and the approximate cost of the path between some pairs of nodes in the original network. We develop two privacy-preserving strategies for this application. The first strategy is based on a Gaussian randomization multiplication, the second one is a greedy perturbation algorithm based on graph theory. In particular, the second strategy not only yields an approximate length of the shortest path while maintaining the shortest path between selected pairs of nodes, but also maximizes privacy preservation of the original weights. We present experimental results to support our mathematical analysis.
As grid computing becomes a reality, a resource broker is needed to manage and monitor available resources. This work presents a workflow-based computational resource broker whose main function is to match available r...
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As grid computing becomes a reality, a resource broker is needed to manage and monitor available resources. This work presents a workflow-based computational resource broker whose main function is to match available resources with user requests and consider network information status during matchmaking. The resource broker provides a uniform interface for accessing available and the appropriate resources via user credentials. We utilize the NWS tool to monitor the network-related information and resources status. Also, we constructed a grid platform using Globus Toolkit that integrates the distributed resources of five universities in Taichung, Taiwan, where the resource broker is developed. As a result, the proposed broker provides secure and updated information about available resources and serves as a link to the diverse systems available in the grid
In this paper, an artificial neural network (ANN) model is proposed to predict the flexibility (or robustness against system load fluctuations in heterogeneous computing systems) of dynamic loop scheduling (DLS) metho...
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Scientific computing has become one of the key players in the advance of modern science and technologies. In the meantime, due to the success of developments in processor fabrication, the computing power of Personal C...
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Scientific computing has become one of the key players in the advance of modern science and technologies. In the meantime, due to the success of developments in processor fabrication, the computing power of Personal computer (PC) is not to be ignored as well. Lots of high throughput type of applications can be satisfied by using the current desktop PCs, especially for those in computerized classrooms, and leave the supercomputers for the demands from large scale highperformance parallel computations. The goal of this work is to develop an automated mechanism for cluster computing to utilize the computing power such as resides in computerized classroom. The PCs in computerized classroom are usually setup for education and training purpose during the daytime, and shut down at night. After well deployment, these PCs can be transformed into a pre-configured cluster computing resource immediately without touching the existing education/training environment installed on these PCs. Thus, the training activities will not be affected by this additional activity to harvest idle computing cycles. To echo today's energy saving issues, a dynamic power management is also developed to minimize energy cost. This development not only greatly reduces the management efforts and time to build a cluster, but also implies the reduction of the power consumption by such a mechanism.
Loop scheduling on parallel and distributed systems has been a critical problem. Furthermore, it becomes more difficult to deal with on the emerging heterogeneous grid environments. In the past, some loop self-schedul...
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Loop scheduling on parallel and distributed systems has been a critical problem. Furthermore, it becomes more difficult to deal with on the emerging heterogeneous grid environments. In the past, some loop self-scheduling schemes have been proposed to be applicable to heterogeneous gird environments. In this paper, we propose a performance-based approach, which partitions loop iterations according to the performance weight of nodes. To verify the proposed approach, a grid testbed that consists four schools is built, and matrix multiplication example is implemented to be executed in this testbed. Experimental results show that the proposed approach performs better than previous schemes.
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