Study on Ant Colony Algorithm about Resource Search and Optimization in Cloud Manufacturing

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Abstract:

These years, a new networked manufacturing mode named cloud manufacturing is arising. Cloud manufacturing comprehensively uses various information technology, manufacturing technology and management technology, through virtualization and servitization of manufacturing hardware and software resources. CloudM is to provide user with on-demand, always-ready, high-quality and low-consumption service, which is available from product design, manufacturing, testing, simulation and maintenance and other manufacturing lifecycle process. Service composition is one of the key issues in implementing CloudM system. In this paper, an adaptive ant algorithms have been proposed, which In hadoop environment to optimize the use of composite service execution path. As the experimental result shown, this method can effectively avoid the congestion of data on the critical path.

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3152-3155

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August 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Xun Xu.  From cloud computing to cloud manufacturing[J]. Robotics and Computer Integrated Manufacturing . 2011 (1).

Google Scholar

[2] . Zhang Yan-hua, Feng Lei, Yang Zhi.  Optimization of Cloud Database Route Scheduling Based on Combination of Genetic Algorithm and Ant Colony Algorithm[J]. Procedia Engineering . (2011).

DOI: 10.1016/j.proeng.2011.08.626

Google Scholar

[3] A Brief Survey on the Security Model of Cloud Computing[A]. Proceedings of the Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science[C]. (2010).

DOI: 10.1109/dcabes.2010.103

Google Scholar

[4] Research and Implementation of Future Network Computer based on Cloud Computing[A]. Proceedings of 2010 Third International Symposium on Knowledge Acquisition and Modeling (KAM 2010)[C]. (2010).

DOI: 10.1109/kam.2010.5646281

Google Scholar

[5] Nezamabadi-pour H, Saryazdi S, and Rashedi E. Edge detection using ant algorithm[J]. Soft Comput, 2006, 10(7): 623-628.

DOI: 10.1007/s00500-005-0511-y

Google Scholar