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Keywords: cloud computing, Google App Engine, genetic algorithm.

Summary

Recently, Cloud Computing has attracted wide attention not only from IT specialists but also from several users[1,2]. Cloud computing is internet-based computing, whereby shared resources, software and information are provided to computers and other devices on-demand, like the electricity grid.

Cloud computing has the following advantages. Firstly it can reduce the cost of IT technologies. Secondly, the flexible scalability of cloud computing is also important. The third is that cloud computing is ubiquitous. On the other hand, it is sometimes a great disadvantage that all data and applications exist on the internet. When the data center of the cloud computing supplier is attacked or the LAN is stopped, all services may stop.

In this study, we will discuss the utility of the cloud computing for scientific computing. Google App Engine (GAE) is adopted because it is free and good software development Kit is available. Simple genetic algorithms (GA)[3] are implemented on the GAE for solving the minimization of De Jong test function[4]. The comparison of the results in GAE and local host indicates the following:

The computational speed in GAE is faster by almost 25% than that in local host although the CPU specification of GAE is lower than that of local host.
The variation of computational speed in the GAE is smaller than that in the local host. This means that the GAE performance dose not depend on the CPU usage.

References

1: M. Miller, "Cloud Computing: Web-Based Applications That Change the Way You Work and Collaborate Online", Que Publishing, 2009.
2: N. Carr, "The Big Switch: Rewiring the World, from Edison to Google", 2009.
3: D.E. Goldberg, "Genetic Algorithms in Search, Optimization and Machine Learning", Addison Wesley, 1989.
4: K.A. De Jong, "An analysis of the behavior of a class of genetic adaptive systems", PhD thesis, University of Michigan, Dissertation Abstracts International, 36(10), 5140B, UMI 76-9381, 1975

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 94 PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE ON ENGINEERING COMPUTATIONAL TECHNOLOGY Edited by: Paper 18 Implementation of Genetic Algorithms on Free Cloud Computing S. Ukai, Y. Zuo and E. Kita Graduate School of Information Science, Nagoya University, Japan doi:10.4203/ccp.94.18 purchase the full-text of this paper Full Bibliographic Reference for this paper S. Ukai, Y. Zuo, E. Kita, "Implementation of Genetic Algorithms on Free Cloud Computing", in , (Editors), "Proceedings of the Seventh International Conference on Engineering Computational Technology", Civil-Comp Press, Stirlingshire, UK, Paper 18, 2010. doi:10.4203/ccp.94.18 Keywords: cloud computing, Google App Engine, genetic algorithm. Summary Recently, Cloud Computing has attracted wide attention not only from IT specialists but also from several users[1,2]. Cloud computing is internet-based computing, whereby shared resources, software and information are provided to computers and other devices on-demand, like the electricity grid. Cloud computing has the following advantages. Firstly it can reduce the cost of IT technologies. Secondly, the flexible scalability of cloud computing is also important. The third is that cloud computing is ubiquitous. On the other hand, it is sometimes a great disadvantage that all data and applications exist on the internet. When the data center of the cloud computing supplier is attacked or the LAN is stopped, all services may stop. In this study, we will discuss the utility of the cloud computing for scientific computing. Google App Engine (GAE) is adopted because it is free and good software development Kit is available. Simple genetic algorithms (GA)[3] are implemented on the GAE for solving the minimization of De Jong test function[4]. The comparison of the results in GAE and local host indicates the following: The computational speed in GAE is faster by almost 25% than that in local host although the CPU specification of GAE is lower than that of local host. The variation of computational speed in the GAE is smaller than that in the local host. This means that the GAE performance dose not depend on the CPU usage. References 1 M. Miller, "Cloud Computing: Web-Based Applications That Change the Way You Work and Collaborate Online", Que Publishing, 2009. 2 N. Carr, "The Big Switch: Rewiring the World, from Edison to Google", 2009. 3 D.E. Goldberg, "Genetic Algorithms in Search, Optimization and Machine Learning", Addison Wesley, 1989. 4 K.A. De Jong, "An analysis of the behavior of a class of genetic adaptive systems", PhD thesis, University of Michigan, Dissertation Abstracts International, 36(10), 5140B, UMI 76-9381, 1975 purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £125 +P&P)
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