A study of optimal allocation of computing resources in cloud manufacturing systems

As a new advanced service-oriented networked manufacturing model, cloud manufacturing (CMfg) has been proposed recently. The optimal allocation of computing resources (OACR) is a core part for implementing CMfg. High heterogeneity, high dynamism, and virtualization make the OACR problem more complex than the traditional scheduling problems in grid system or cloud computing system. In this paper, a new comprehensive model for OACR is proposed in the CMfg system. In this model, all main computation, communication, and reliability constraints in the special circumstances are considered. To solve the OACR problem, a new improved niche immune algorithm was presented. Associated with the niche strategy, new heuristics are designed flexibly based on the characteristics of the problem and pheromone is added for adaptive searching. Experiments demonstrate the effectiveness of the designed heuristic information and show NIA’s high performances for addressing the OACR problem compared with other intelligent algorithms.

[1]  Tilman Wolf,et al.  Profiling and mapping of parallel workloads on network processors , 2005, SAC '05.

[2]  Tao Yang,et al.  On the Granularity and Clustering of Directed Acyclic Task Graphs , 1993, IEEE Trans. Parallel Distributed Syst..

[3]  Ren Lei,et al.  Typical characteristics of cloud manufacturing and several key issues of cloud service composition , 2011 .

[4]  Fei Tao,et al.  Study on manufacturing grid resource service QoS modeling and evaluation , 2009 .

[5]  A. Gunasekaran,et al.  Agile manufacturing: The drivers, concepts and attributes , 1999 .

[6]  Viktor K. Prasanna,et al.  Efficient collective communication in distributed heterogeneous systems , 2003, J. Parallel Distributed Comput..

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

[8]  Tung-Kuan Liu,et al.  An improved genetic algorithm for job-shop scheduling problems using Taguchi-based crossover , 2008 .

[9]  Manish Madhukar,et al.  Petri net model of a dynamically partitioned multiprocessor system , 1995, Proceedings 6th International Workshop on Petri Nets and Performance Models.

[10]  Tao Yang,et al.  DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors , 1994, IEEE Trans. Parallel Distributed Syst..

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

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

[13]  Fei Tao,et al.  Resource Service Management in Manufacturing Grid System: Tao/Resource , 2011 .

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

[15]  Quan-Ke Pan,et al.  Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems , 2011 .

[16]  Mary Mehrnoosh Eshaghian-Wilner,et al.  Mapping Arbitrary Non-Uniform Task Graphs onto Arbitrary Non-Uniform System Graphs , 1995, ICPP.

[17]  Leonel Sousa,et al.  List scheduling: extension for contention awareness and evaluation of node priorities for heterogeneous cluster architectures , 2004, Parallel Comput..

[18]  W. Lei.  The Immune Programming , 2000 .

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

[20]  K. Mani Chandy,et al.  A comparison of list schedules for parallel processing systems , 1974, Commun. ACM.

[21]  Po-Chieng Hu,et al.  Minimizing total flow time for the worker assignment scheduling problem in the identical parallel-machine models , 2005 .

[22]  Harold S. Stone,et al.  Multiprocessor Scheduling with the Aid of Network Flow Algorithms , 1977, IEEE Transactions on Software Engineering.

[23]  Giovanni Flammia.  Application Service Providers: Challenges and Opportunities , 2001, IEEE Intell. Syst..

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

[25]  Fei Tao,et al.  Resource Service Composition and Its Optimal-Selection Based on Particle Swarm Optimization in Manufacturing Grid System , 2008, IEEE Transactions on Industrial Informatics.

[26]  Ling Wang,et al.  Solving the blocking flow shop scheduling problem by a dynamic multi-swarm particle swarm optimizer , 2011 .

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

[28]  Viktor K. Prasanna,et al.  Distributed adaptive task allocation in heterogeneous computing environments to maximize throughput , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[29]  Andrew Y. C. Nee,et al.  A review of the application of grid technology in manufacturing , 2011 .

[30]  R. Saravanan,et al.  Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm , 2006 .

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

[32]  Afonso Ferreira,et al.  Scheduling Multiprocessor Tasks with Genetic Algorithms , 1999, IEEE Trans. Parallel Distributed Syst..

[33]  Fei Tao,et al.  Study on resource service match and search in manufacturing grid system , 2009 .

[34]  Manoj Kumar Tiwari,et al.  Fuzzy-based adaptive sample-sort simulated annealing for resource-constrained project scheduling , 2008 .

[35]  Ze-ming Zou,et al.  Integrated and events-oriented job shop scheduling , 2006 .

[36]  Tao Yang,et al.  Performance bounds for column-block partitioning of parallel Gaussian elimination and Gauss-Jordan methods , 1994 .

[37]  Kyo-Il Lee,et al.  An intelligent operations scheduling system in a job shop , 1996 .

[38]  Jiao Li-cheng.  The Immune Algorithm , 2000 .

[39]  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).

[40]  Liang Yang,et al.  Resource mapping and scheduling for heterogeneous network processor systems , 2005, 2005 Symposium on Architectures for Networking and Communications Systems (ANCS).

[41]  Shahid H. Bokhari.  Dual Processor Scheduling with Dynamic Reassignment , 1979, IEEE Transactions on Software Engineering.

[42]  Chandrabose Aravindan,et al.  A meta-heuristic approach to single machine scheduling problems , 2005 .

[43]  Ji-Bo Wang,et al.  Two single-machine scheduling problems with the effects of deterioration and learning , 2010 .

[44]  Fei Tao,et al.  Cloud manufacturing: a computing and service-oriented manufacturing model , 2011 .

[45]  Zhang Lin,et al.  Further discussion on cloud manufacturing , 2011 .

[46]  Limin Jiao,et al.  An intelligent concurrent design task planner for manufacturing systems , 2004 .

[47]  Congxin Li,et al.  Research on immune genetic algorithm for solving the job-shop scheduling problem , 2007 .

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

[49]  Vivek Sarkar,et al.  Partitioning and Scheduling Parallel Programs for Multiprocessing , 1989 .

[50]  Constantine D. Polychronopoulos,et al.  The hierarchical task graph and its use in auto-scheduling , 1991, ICS '91.

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

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

[53]  A. Noorul Haq,et al.  Evaluation of scatter-search approach for scheduling optimization of flexible manufacturing systems , 2008 .

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

[55]  Fei Tao,et al.  An approach to manufacturing grid resource service scheduling based on trust-QoS , 2009, Int. J. Comput. Integr. Manuf..

[56]  Yu-Wang Chen,et al.  Hybrid evolutionary algorithm with marriage of genetic algorithm and extremal optimization for production scheduling , 2008 .

[57]  Fei Tao,et al.  Study on manufacturing grid & its resource service optimal-selection system , 2008 .

[58]  Daniel Gajski,et al.  Hypertool: A Programming Aid for Message-Passing Systems , 1990, IEEE Trans. Parallel Distributed Syst..

[59]  Fei Tao,et al.  Correlation-aware resource service composition and optimal-selection in manufacturing grid , 2010, Eur. J. Oper. Res..

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

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

[62]  TATJANA DAVIDOVIĆ,et al.  Permutation-Based Genetic, Tabu, and Variable Neighborhood Search Heuristics for Multiprocessor Scheduling with Communication delays , 2004, Asia Pac. J. Oper. Res..

[63]  Fei Tao,et al.  Resource service optimal-selection based on intuitionistic fuzzy set and non-functionality QoS in manufacturing grid system , 2010, Knowledge and Information Systems.

[64]  Guo Hua,et al.  Key technologies for the construction of manufacturing cloud , 2010 .

[65]  Fei Tao,et al.  Application and modeling of resource service trust-QoS evaluation in manufacturing grid system , 2009 .

[66]  Fei Tao,et al.  Study of failure detection and recovery in manufacturing grid resource service scheduling , 2010 .

[67]  Manoj Kumar Tiwari,et al.  Artificial immune system based approach for solving resource constraint project scheduling problem , 2007 .

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

[69]  Paul R. Drake,et al.  Minimizing total tardiness for the machine scheduling and worker assignment problems in identical parallel machines using genetic algorithms , 2009 .