Grid Computing based on Game Optimization Theory for Networks Scheduling

The resource sharing mechanism is introduced into grid computing algorithm so as to solve complex computational tasks in heterogeneous network-computing problem. However, in the Grid environment, it is required for the available resource from network to reasonably schedule and coordinate, which can get a good workflow and an appropriate network performance and network response time. In order to improve the performance of resource allocation and task scheduling in grid computing method, a game model based on non-cooperation game is proposed. Setting the time and cost of user’s resource allocation can increase the performance of networks, and incentive resource of networks uses an optimization scheduling algorithm, which minimizes the time and cost of resource scheduling. Simulation experiment results show the feasibility and suitability of model. In addition, we can see from the experiment result that model-based genetic algorithm is the best resource scheduling algorithm

[1]  Kaijian Xia,et al.  Adaptive error control mechanism based on link layer frame importance valuation for wireless multimedia sensor networks , 2010, 2010 2nd International Conference on Advanced Computer Control.

[2]  Siamak Khorram,et al.  A feature-based image registration algorithm using improved chain-code representation combined with invariant moments , 1999, IEEE Trans. Geosci. Remote. Sens..

[3]  George Wolberg,et al.  Robust image registration using log-polar transform , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[4]  J C Mazziotta,et al.  Automated image registration: II. Intersubject validation of linear and nonlinear models. , 1998, Journal of computer assisted tomography.

[5]  Jin-yi Chang,et al.  An edge detection improved algorithm based on morphology and wavelet transform , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[6]  Benoit M. Dawant,et al.  The adaptive bases algorithm for intensity-based nonrigid image registration , 2003, IEEE Transactions on Medical Imaging.

[7]  Max A. Viergever,et al.  Image registration by maximization of combined mutual information and gradient information , 2000, IEEE Transactions on Medical Imaging.

[8]  Max A. Viergever,et al.  Image Registration by Maximization of Combined Mututal Information and Gradient Information , 2000, MICCAI.

[9]  Jian Cai,et al.  Research on Improved Network Data Fault-Tolerant Transmission Optimization Algorithm , 2012 .

[10]  Manuel Guizar-Sicairos,et al.  Efficient subpixel image registration algorithms. , 2008, Optics letters.

[11]  Karl J. Friston,et al.  Incorporating Prior Knowledge into Image Registration , 1997, NeuroImage.

[12]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[13]  Joe Y. Chang,et al.  Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy , 2005, Physics in medicine and biology.

[14]  Terry S. Yoo,et al.  Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis , 2004 .

[15]  Gary E. Christensen,et al.  Consistent image registration , 2001, IEEE Transactions on Medical Imaging.

[16]  Michael Unser,et al.  Optimization of mutual information for multiresolution image registration , 2000, IEEE Trans. Image Process..

[17]  Max A. Viergever,et al.  Interpolation Artefacts in Mutual Information-Based Image Registration , 2000, Comput. Vis. Image Underst..

[18]  R W Cox,et al.  Real‐time 3D image registration for functional MRI , 1999, Magnetic resonance in medicine.

[19]  Nicholas Ayache,et al.  The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration , 1998, MICCAI.

[20]  David R. Haynor,et al.  PET-CT image registration in the chest using free-form deformations , 2003, IEEE Transactions on Medical Imaging.

[21]  Kaijian Xia,et al.  A Case of Parallel EEG Data Processing upon a Beowulf Cluster , 2009, 2009 15th International Conference on Parallel and Distributed Systems.

[22]  J. F. Bradshaw,et al.  The principal axes transformation--a method for image registration. , 1990, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[23]  Nick McKeown,et al.  Algorithms for packet classification , 2001, IEEE Netw..

[24]  B. N. Chatterji,et al.  An FFT-based technique for translation, rotation, and scale-invariant image registration , 1996, IEEE Trans. Image Process..

[25]  Kaijian Xia,et al.  Research in Clustering Algorithm for Diseases Analysis , 2013, J. Networks.

[26]  Kaijian Xia,et al.  The representation and simulation for reasoning about action based on Colored Petri Net , 2010, 2010 2nd IEEE International Conference on Information Management and Engineering.

[27]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .