In this paper, a reputation-based Grid workflow scheduling algorithm is proposed to counter the effect of inherent unreliability and temporal characteristics of computing resources in large scale, decentralized Grid o...
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Efficient scheduling is a key concern for the effectual execution of performance driven Grid applications, such as workflows. Many list heuristics have been developed for scheduling workflows in centralized Grid envir...
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Ambulatory electrocardiogram is used to record about 24 hours electrocardiogram waveform. The purpose is to find out the conspicuous variation in the recording data. A new strategy used to cluster the electrocardiogra...
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With the growth of Utility Grids and various Grid market infrastructures, the need for efficient and cost effective scheduling algorithms is also increasing rapidly, particularly in the area of meta-scheduling. In the...
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Assigning jobs to optimum resources in a grid environment is the main aim of a grid scheduler. Communication cost has always been an important issue in grid environments. Proposing new scheduling algorithms to conside...
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Assigning jobs to optimum resources in a grid environment is the main aim of a grid scheduler. Communication cost has always been an important issue in grid environments. Proposing new scheduling algorithms to consider this cost accurately and allocate jobs to an optimum resource efficiently has always been of great importance. In this paper we have proposed a grid scheduling algorithm that is aware of the costs for different network *** have also considered soft realtime characteristics of jobs in our proposed algorithm. We have simulated and compared our algorithm with some key scheduling algorithms such as Least Load First (LLF), Random, and First Come First Served (FCFS) in Gridsim by considering soft real-time jobs. Results show the superiority of our scheduling algorithm due to its ability to predict network cost and to satisfy the demands of soft real-time tasks.
Based on evolutionary programming, a universal approach to examine the non-analytic solution robustness of an optimal allocation methodology for industrial boilers that is designed based on the second-order gradient m...
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Existing software process models such as Waterfall and XP are characterised by unstated assumptions, a consequence of which is that we can not easily compare models or transfer data from one model to another. This mea...
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The highlight line model is a powerful tool in assessing the quality of a surface. It increases the flexibility of an interactive design environment. In this paper, a method to generate a highlight line model on an ar...
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The highlight line model is a powerful tool in assessing the quality of a surface. It increases the flexibility of an interactive design environment. In this paper, a method to generate a highlight line model on an arbitrary triangular mesh is presented. Based on the highlight line model, a technique to remove local shape irregularities of a triangular mesh is then presented. The shape modification is done by solving a minimization problem and performing an iterative procedure. The new technique improves not only the shape quality of the mesh surface, but also the shape of the highlight line model. It provides an intuitive and yet suitable method for locally optimizing the shape of a triangular mesh.
Though K-means is very popular for general clustering, its performance, which generally converges to numerous local minima, depends highly on initial cluster centers. In this paper a novel initialization scheme to sel...
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Though K-means is very popular for general clustering, its performance, which generally converges to numerous local minima, depends highly on initial cluster centers. In this paper a novel initialization scheme to select initial cluster centers for K-means clustering is proposed. This algorithm is based on reverse nearest neighbor （RNN） search which retrieves all points in a given data set whose nearest neighbor is a given query point. The initial cluster centers computed using this methodology are found to be very close to the desired cluster centers for iterative clustering algorithms. This procedure is applicable to clustering algorithms for continuous data. The application of the proposed algorithm to K-means clustering algorithm is demonstrated. An experiment is carried out on several popular datasets and the results show the advantages of the proposed method.
Data mining on uncertain data stream has attracted a lot of attentions because of the widely existed imprecise data generated from a variety of streaming applications in recent years. The main challenge of mining unce...
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