The optimal allocation model of computing resources in cloud manufacturing system

Recently, a new advanced service-oriented networked manufacturing model: Cloud Manufacturing (CMfg) has been proposed. The optimal allocation of computing resources (OACR) for the whole life-circle of manufacturing is an important part of CMfg. In order to implement optimal allocation, A new model for the computing resources and manufacturing tasks in CMfg is presented in this study, in which both the computation and communication factors are considered, and the formalized description on their dynamic process is given out. Primary constraints were introduced and the optimal objective of the model was proposed. Then the standard genetic algorithm (GA) was introduced to OACR. Experiments illustrated the effect of the constraints on the model and the application of GA on this problem.

[1]  Jeffrey D. Ullman,et al.  NP-Complete Scheduling Problems , 1975, J. Comput. Syst. Sci..

[2]  Jemal H. Abawajy,et al.  Parallel job scheduling on multicluster computing system , 2003, 2003 Proceedings IEEE International Conference on Cluster Computing.

[3]  Rainer Kolisch,et al.  PSPLIB - A project scheduling problem library: OR Software - ORSEP Operations Research Software Exchange Program , 1997 .

[4]  Huang Jin Parallel-job Scheduling on Cluster Computing Systems , 2004 .

[5]  Zhou Qi-feng Research on Grid Resource Management and Scheduling , 2009 .

[6]  Jun Zhang,et al.  An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[7]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[8]  Frode Eika Sandnes,et al.  Toward a realistic task scheduling model , 2006, IEEE Transactions on Parallel and Distributed Systems.

[9]  Gang Wang,et al.  Ant Colony Optimizations for Resource- and Timing-Constrained Operation Scheduling , 2007, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[10]  Ramin Yahyapour,et al.  Design and evaluation of job scheduling strategies for grid computing , 2000, GRID.

[11]  David Abramson,et al.  The Grid Economy , 2005, Proceedings of the IEEE.

[12]  Ramin Yahyapour,et al.  On Effects of Machine Configurations on Parallel Job Scheduling in Computational Grids , 2002 .

[13]  Thomas Bäck,et al.  Evolutionary computation: comments on the history and current state , 1997, IEEE Trans. Evol. Comput..

[14]  Leonel Sousa,et al.  Communication contention in task scheduling , 2005, IEEE Transactions on Parallel and Distributed Systems.

[15]  Stanislaw Gawiejnowicz,et al.  Time-Dependent Scheduling , 2008, Monographs in Theoretical Computer Science. An EATCS Series.

[16]  Zheng Chao Survey of research progress on cloud computing , 2010 .

[17]  Daniel C. Stanzione,et al.  Characterization of Bandwidth-Aware Meta-Schedulers for Co-Allocating Jobs Across Multiple Clusters , 2005, The Journal of Supercomputing.

[18]  Gang Wang,et al.  Ant Colony Optimizations for Resource- and Timing-Constrained Operation Scheduling , 1997, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[19]  Huang Jin-gui Approximation algorithm on multi-processor job scheduling , 2008 .

[20]  Yves Robert,et al.  Scheduling Concurrent Bag-of-Tasks Applications on Heterogeneous Platforms , 2010, IEEE Transactions on Computers.

[21]  Ishfaq Ahmad,et al.  Benchmarking and Comparison of the Task Graph Scheduling Algorithms , 1999, J. Parallel Distributed Comput..

[22]  Chai Xu-dong,et al.  Cloud manufacturing:a new service-oriented networked manufacturing model , 2010 .

[23]  Amit P. Sheth,et al.  Modeling Quality of Service for Workflows and Web Service Processes , 2002 .