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Computing Resource Allocation with PEADGA

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Configurable Intelligent Optimization Algorithm

Part of the book series: Springer Series in Advanced Manufacturing ((SSAM))

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Abstract

In this chapter, for solving optimal allocation of computing resources (OACR) problem in cloud manufacturing (CMfg) [1], serial three-layer operation configuration and parallel configuration are both applied.

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References

  1. Li BH, Zhang L, Wang SL, Tao F, Cao JW, Jiang XD, Song X, Chai XD (2010) Cloud manufacturing: a new service-oriented networked manufacturing model. Comput Integr Manuf Syst 16(1):1–16

    Google Scholar 

  2. Laili YJ, Tao F, Zhang L, Sarker BR (2012) A study of optimal allocation of computing resources in cloud manufacturing systems. Int J Adv Manuf Technol 63(5–8):671–690

    Article  Google Scholar 

  3. Yusuf YY, Sarhadi M, Gunasekaran A (1999) Agile manufacturing: The drivers, concepts and attributed. Int J Prod Econ 62(1–2):33–43

    Article  Google Scholar 

  4. Flammia G (2001) Application service providers: challenges and opportunities. IEEE Intell Syst Appl 16(1):22–23

    Article  Google Scholar 

  5. Tao F, Hu YF, Zhou ZD (2008) Study on manufacturing grid & its resource service optimal-selection system. Int J Adv Manuf Technol 37(9–10):1022–1041

    Article  Google Scholar 

  6. Tao F, Zhang L, Venkatesh VC, Luo YL, Cheng Y (2011) Cloud manufacturing: a computing and service-oriented manufacturing model. Proc Inst Mech Eng, Part B, J Eng Manuf (2011, March 10, Accepted)

    Google Scholar 

  7. Zhang L, Luo LY, Tao F, Ren L, Guo H (2010) Key technologies for the construction of manufacturing cloud. Comput Integr Manuf Syst 16(11):2510–2520

    Google Scholar 

  8. He K, Zhao Y (2005) Research of grid resource management and scheduling. J WuHan Univ Technol (Information and Management Engineering) 27(4): 1–5

    Google Scholar 

  9. Ullman JD (1975) NP-complete scheduling problems. J Comput Syst Sci 10(3):384–393

    Article  MATH  MathSciNet  Google Scholar 

  10. Zhang L, Luo YL, Fan WH, Tao F, Ren L (2011) Analysis of cloud manufacturing and related advanced manufacturing models. Comput Integr Manuf Syst 17(3):458–468

    Google Scholar 

  11. Li BH, Zhang L, Chai XD, Tao F, Luo YL, Wang YZ, Yin C, Huang G, Zhao XP (2011) Further discussion on cloud manufacturing. Comput Integr Manuf Syst 27(3):449–457

    Google Scholar 

  12. Tao F, Cheng Y, Zhang L, Luo YL, Ren L (2011) Cloud manufacturing. The 2nd international conference on manufacturing service and engineering (ICMSE)

    Google Scholar 

  13. Tao F, Zhang L, Luo YL, Ren L (2011) Typical characteristic of cloud manufacturing and several key issues of cloud service composition. Comput Integr Manuf Syst 17(3):477–486

    Google Scholar 

  14. Liang JJ, Pan QK, Chen TJ, Wang L (2011) Solving the blocking flow shop scheduling problem by a dynamic multi-swarm particle swarm optimizer. Int J Adv Manuf Technol 55(5–8):755–762

    Article  Google Scholar 

  15. Zou ZM, Li CX (2006) Integrated and events-oriented job shop scheduling. Int J Adv Manuf Technol 29(5–6):551–556

    Article  Google Scholar 

  16. Hu PC (2005) Minimizing total flow time for the worker assignment scheduling problem in the identical parallel-machine models. Int J Adv Manuf Technol 25(9–10):1046–1052

    Article  Google Scholar 

  17. Kwok YK (1999) Benchmarking and comparison of the task graph scheduling algorithms. J Parallel Distrib Comput 59(3):381–422

    Article  MATH  Google Scholar 

  18. Polychronopoulos CD (1991) The hierarchical task graph and its use in auto-scheduling. In: Proceedings of the 5th international conference on supercomputing (ICS’ 91)

    Google Scholar 

  19. Bokhari SH (1979) Dual processor scheduling with dynamic reassignment. IEEE Trans Software Eng 5(4):341–349

    Article  MathSciNet  Google Scholar 

  20. Stone HS (1977) Multiprocessor scheduling with the aid of network flow algorithms. IEEE Trans Software Eng 3(1):85–93

    Article  MATH  Google Scholar 

  21. Madhukar M, Leuze V, Dowdy V (1995) Petri net model of a dynamically partitioned multiprocessors system. In: Proceedings of the 6th international workshop on petri nets and performance models (PNPM’ 95)

    Google Scholar 

  22. Buyya R, Abramson D, Venugopal S (2005) The grid economy. Proc IEEE 93(3):698–714

    Article  Google Scholar 

  23. Cardoso J, Sheth A, Miller J, Arnold J, Kochut K (2004) Quality of service for workflows and web service processes. Web Semant: Sci Serv Agents WWW 1(3):281–308

    Article  Google Scholar 

  24. Yang T, Gerasoulis A (1993) DSC: Scheduling parallel tasks on an unbounded number of processors. IEEE Trans Parallel Distrib Syst 5(9):951–967

    Article  Google Scholar 

  25. Gerasoulis A, Yang T (1993) On the granularity and clustering of directed acyclic task graphs. IEEE Trans Parallel Distrib Syst 4(6):686–701

    Article  Google Scholar 

  26. Gerasoulis A, Yang T (1994) Performance bounds for parallelizing Gaussian-Elimination and Gauss-Jordan on message-passing machines. Applied Numerical Mathematics Journal 16:283–297

    Article  MATH  MathSciNet  Google Scholar 

  27. Jones WM, Pang LW, Ligon WB, Stanzione D (2005) Characterization of bandwidth-aware meta-schedulers for co-allocating jobs across multiple clusters. J Supercomput 34(2):135–163

    Article  Google Scholar 

  28. Hamscher V, Schwiegelshohn U, Streit A, Yahyapour V (2004) Evaluation of job-scheduling strategies for grid computing. Grid Computing at the 7th International Conference on High Performance Computing 191–202

    Google Scholar 

  29. Ememann C, Hamscher V, Yahyapou V (2002) On effects of machine configurations on parallel job scheduling in computational grids. In: Proceedings of the international conference on architecture of computing systems (ARCS 2002), 169–179

    Google Scholar 

  30. Davidovi T, Hansen P, Mladenovi N (2005) Permutation based genetic, tabu and variable neighborhood search heuristics for multiprocessor scheduling with communication delays. Asia-Pac J Oper Res 22(3):297–326

    MathSciNet  Google Scholar 

  31. Sinnen O, Sousa LA (2005) Communication contention in task scheduling. IEEE Trans Parallel Distrib Syst 16(6):503–515

    Article  Google Scholar 

  32. Sinnen O, Sousa LA, Sandnes FE (2006) Toward a realistic task scheduling model. IEEE Trans Parallel Distrib Syst 17(3):263–275

    Article  Google Scholar 

  33. Benoit A, Marchal L, Pineau JF (2010) Scheduling concurrent bag-of-tasks applications on heterogeneous platforms. IEEE Trans Comput 59(2):202–217

    Article  MathSciNet  Google Scholar 

  34. Adam TL, Chandy KM, Dickson JR (1974) A comparison of list schedules for parallel processing systems. Commun ACM 17(12):685–690

    Article  MATH  Google Scholar 

  35. Sinnen O, Sousa LA (2004) List scheduling: Extension for contention awareness and evaluation of node priorites for heterogeneous cluster architectures. Parallel Comput 30(1):81–101

    Article  Google Scholar 

  36. Wu MY, Gajski DD (1990) Hypertool: a programming aid for message-passing systems. IEEE Trans Parallel Distrib Syst 1(3):330–343

    Article  Google Scholar 

  37. Sarkar V (1989) Partitioning and scheduling of parallel programs for multiprocessors. Research Monographs in Parallel Computing, MIT Press

    Google Scholar 

  38. Chen S, Eshaghia MM, Wu Y (1995) Mapping arbitrary non-uniform task graphs onto arbitrary non-uniform system graphs. In: Proceedings of the international conference on parallel processing

    Google Scholar 

  39. Yang L, Gohad T, Ghosh P, Sinha D, Sen D, Richa A (2005) Resource mapping and scheduling for heterogeneous network processor systems. In: Proceedings of the 2005 ACM Symposium on Architecture for Networking and Communications Systems (ANCS’ 05), 19–28

    Google Scholar 

  40. Weng N, Wolf T (2005) Profiling and mapping of parallel workloads on network processors. In: proceedings of the 20th annual ACM symposium on applied computing (sac) 890–896

    Google Scholar 

  41. Huang JG, Chen JE, Chen SQ (2004) Parallel-job scheduling on cluster computing system. Chin J Comput 27(6):765–771

    Google Scholar 

  42. Huang JG (2008) Approximation algorithm on multi-processor job scheduling. Comput Eng Appl 44(32):26–28

    Google Scholar 

  43. Yin GF, Luo Y, Long HN, Cheng EJ (2004) Genetic algorithms for subtask scheduling in concurrent design. J Comput aided Des Comput Graph 16(8): 1122–1126

    Google Scholar 

  44. Correa RC, Ferreira A, Rebreyend P (1999) Scheduling multiprocessor tasks with genetic algorithms. IEEE Trans Parallel Distrib Syst 10(8):825–837

    Article  Google Scholar 

  45. Tsai JT, Liu TK, Ho WH, Chou JH (2008) An improved genetic algorithm for job-shop scheduling problems using taguchi-based crossover. Int J Adv Manuf Technol 38(9–10):987–994

    Article  Google Scholar 

  46. Chen YW, Lu YZ, Yang GK (2008) Hybrid evolutionary algorithm with marriage of genetic algorithm and extremal optimization for production scheduling. Int J Adv Manuf Technol 36(9–10):959–968

    Article  Google Scholar 

  47. Wang G, Gong WR, DeRenzi B, Kastner R (2007) Ant colony optimizations for resource and timing constrained operation scheduling. IEEE Trans Comput Aided Des Integr Circuits Syst 26(6):1010–1029

    Article  Google Scholar 

  48. Chen WN, Zhang J (2009) An ant colony optimization approach to a grid workflow scheduling problem with various QoS requirements. IEEE Trans Syst Man Cyber 39(1):29–43

    Google Scholar 

  49. Li JQ, Pan QK, Gao KZ (2011) Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. Int J Adv Manuf Technol 55(9–12):1159–1169

    Article  Google Scholar 

  50. Xu XD, Li CX (2007) Research on immune genetic algorithm for solving the job-shop scheduling problem. Int J Adv Manuf Technol 34(7–8):783–789

    Article  Google Scholar 

  51. Agarwal R, Tiwari MK, Mukherjee SK (2007) Artificial immune system based approach for solving resource constraint project scheduling problem. Int J Adv Manuf Technol 34(5–6):584–593

    Article  Google Scholar 

  52. Saravanan M, Haq AN (2008) Evaluation of scatter-search approach for scheduling optimization of flexible manufacturing systems. Int J Adv Manuf Technol 38(9–10):978–986

    Article  Google Scholar 

  53. Laha D, Chakraborty UK (2008) An efficient heuristic approach to total flowtime minimization in permutation flow shop scheduling. Int J Adv Manuf Technol 38(9–10):1018–1025

    Article  Google Scholar 

  54. Maheswaran R, Ponnambalam SG, Aravindan C (2005) A meta-heuristic approach to single machine scheduling problems. Int J Adv Manuf Technol 25(7–8):772–776

    Article  Google Scholar 

  55. Zhang JX, Gu ZM, Zheng C (2010) Survey of research progress on cloud computing. Appl Research Comput 27(2): 429–433

    Google Scholar 

  56. Hong B, Prasanna VK (2004) Distributed adaptive task allocation in heterogeneous computing environments to maximize throughput. In: Proceedings of the 18th international parallel and distributed processing symposium (IPDPS’ 04)

    Google Scholar 

  57. Bhat PB, Raghavendra CS, Prasanna VK (2003) Efficient collective communication in distributed heterogeneous systems. J Parallel Distrib Comput 63(3):251–263

    Article  MATH  Google Scholar 

  58. Gawiejnowics S (2008) Time-dependent scheduling. Springer, Berlin

    Google Scholar 

  59. Wang L, Pan J, Jiao LC (2000) The immune programming. Chin J Comput 23(8): 806–812

    Google Scholar 

  60. Wang L, Pan J, Jiao LC (2000). The immune algorithm. Acta Electronica Sinica, 28(7): 74–77

    Google Scholar 

  61. Park J, Kang M, Lee K (1996) An intelligent operations scheduling system in a job shop. Int J Adv Manuf Technol 11(2):111–119

    Article  Google Scholar 

  62. Jiao LM, Khoo LP, Chen CH (2004) An intelligent concurrent design task planner for manufacturing system. Int J Adv Manuf Technol 23(9–10):672–681

    Article  Google Scholar 

  63. Chaudhry IA, Drake PR (2009) Minimizing total tardiness for the machine scheduling and worker assignment problems in identical parallel machines using genetic algorithms. Int J Adv Manuf Technol 42(5–6):581–594

    Article  Google Scholar 

  64. Saravanan M, Haq AN (2008) Evaluation of scatter-search approach for scheduling optimization of flexible manufacturing systems. Int J Adv Manuf Technol 38(9–10):978–986

    Article  Google Scholar 

  65. Wang LY, Wang JB, Gao WJ, Huang X, Feng EM (2010) Two single-machine scheduling problems with the effects of deterioration and learning. Int J Adv Manuf Technol 46(5–8):715–720

    Article  Google Scholar 

  66. Jerald J, Asokan P, Saravanan R, Delphin R, Rani C (2006) Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm. Int J Adv Manuf Technol 29(5–6):584–589

    Article  Google Scholar 

  67. Shukla SK, Son YJ, Tiwari MK (2008) Fuzzy-based adaptive sample-sort simulated annealing for resource-constrained project scheduling. Int J Adv Manuf Technol 36(9–10):982–995

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, China

    Yuanjun Laili, Fei Tao & Lin Zhang

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  1. Yuanjun Laili
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  2. Fei Tao
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  3. Lin Zhang
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Correspondence to Yuanjun Laili .

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Laili, Y., Tao, F., Zhang, L. (2015). Computing Resource Allocation with PEADGA. In: Configurable Intelligent Optimization Algorithm. Springer Series in Advanced Manufacturing. Springer, Cham. https://doi.org/10.1007/978-3-319-08840-2_10

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  • DOI: https://doi.org/10.1007/978-3-319-08840-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08839-6

  • Online ISBN: 978-3-319-08840-2

  • eBook Packages: EngineeringEngineering (R0)

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