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Memetic Computing

, Volume 11, Issue 4, pp 357–370 | Cite as

An intelligent scheduling algorithm for complex manufacturing system simulation with frequent synchronizations in a cloud environment

  • Feng Yao
  • Yiping Yao
  • Lining XingEmail author
  • Huangke Chen
  • Zhongwei Lin
  • Tianlin Li
Regular Research Paper
First Online:

Abstract

For cloud-based, large-scale complex manufacturing system simulation (CMSS), allocating appropriate service instances (virtual machines or nodes) is a promising way to improve execution efficiency. However, the complex interactions among and frequent aperiodic synchronizations of the entities of a CMSS make it challenging to estimate the influence of service instances’ computing power and network latency on the execution efficiency. This hinders the appropriate allocation of service instances for CMSS. To solve this problem, we construct a performance estimation model (PEM) using the executed events and synchronization algorithms to evaluate the running time of CMSS on different service instance combinations. Further, an intelligent scheduling algorithm that introduces PEM as fitness function is proposed to search for a near-optimal allocation scheme of CMSS service instances. To be specific, the PEM-based optimization algorithm (PEMOA) incorporates simulated annealing into the mutation phase of a genetic algorithm to strengthen its local searching ability. A series of experiments were performed on a computer cluster to compare the proposed PEMOA with two representative algorithms: an adapted first-come-first-service-based and the max-min-based allocation algorithms. The experimental results demonstrate that the PEMOA can reduce the running time by more than 7%. In particular, the improvement of PEMOA increases when the manufacturing system simulation is communication-intensive or spans a small number of service instance combinations.

Keywords

Frequent synchronizations Intelligent manufacturing Manufacturing system Performance estimation Parallel and distributed simulation Resource allocation 
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Notes

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (61702527, 61802422, 61773120).

Author Contributions

FY and TL wrote the paper; YY, LX, ZL and HC revised this paper.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Systems EngineeringNational University of Defense TechnologyChangshaChina
  2. 2.College of EngineeringShanghai Polytechnic UniversityShanghaiChina
  3. 3.College of Electronic WarfareNational University of Defense TechnologyHefeiChina

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