Processing Capacity and Response Time Enhancement by Using Iterative Learning Approach with an Application to Insurance Policy Server Operation

Authors

  • Tankut Acarman Galatasaray University
  • Mutlu Ercan AvivaSA Retirement and Life Insurance Company

Keywords:

iterative learning, caching, computational cost model

Abstract

In this study, computing system performance enhancement by using iterative learning technique is presented. Computational response time and throughput of the computing system is improved by introducing computational cost model and selection probability for each individual job. Excepted gain by enforcing dynamic caching is maximized in terms of classifying the arriving computing jobs on an selective manner and dynamically replacing them in a limited memory space. Gain maximization is performed by tuning the window size which helps to compare the computing jobs in terms of their individual selection and occurence probabilities. Fairly special computing work in insurance risk investigation is chosen for experimental validation of the proposed approach. Aspect Oriented Programming (AOP) methodology on Java platform is used for the experimental setup. AOP allows to identify computational jobs and their parameters based on the marked annotations. Experimental results show that the developed iterative learning based caching algorithm performs better than the other well known caching techniques. The design and development of iterative learning based dynamic caching minimizes the necessity of developers' decision about job results to be cached in the memory.

Author Biographies

Tankut Acarman, Galatasaray University

Dr. Acarman received his Ph.D. degree from The Ohio State University, USA in 2002 and then he joined Galatasaray University, computer engineering department where he is currently an associate professor.

Mutlu Ercan, AvivaSA Retirement and Life Insurance Company

Mr. Ercan received his M.Sc. degree in Galatasaray University in 2011.  He has been working in AvivaSA Retirement and Life Insurance Company as a developer.

References

Liu,C.L., Layland, J.W., Scheduling algorithms for multiprogramming in a hard-real-time environment, Journal of Association of Computing Machinery, 20(1), 46 - 61,1973. http://dx.doi.org/10.1145/321738.321743

Jensen, E.D., Locke,C.D. Takuda, H.,A time-driven scheduling model for real-time operating systems, The Proceedings of the 6th IEEE Real-Time Systems Symposium, California, USA,112-122, 1985.

Chen, H., Xia, J., A real-time task scheduling algorithm based on dynamic priority, In- ternational Conference on Embedded Software and Systems, Zhejiang, China, 431 - 436, 2009.

Kantabutra,S., Kornpitak, P., Naramittakapong,C., Dynamic clustering-based round-robin scheduling algorithm, The Proceedings of the 1st Intenational Symposium on Information and Communication Technologies, Dublin, Ireland, 2003.

Nock, C.,Data access patterns: database interactions in object-oriented applications. New York: Addison Wesley, 2003.

Ford, C., Gileadi, I., Purba, S., Moerman, M., Patterns for performance and operability - building and testing enterprise software. Auerbach Publications, 2008.

Padala, P., Shin, K., Zhu, X., Uysal, M., Wang, Z., Singhal, S., Merchant, A., Salem, K.,Adaptive control of virtualized resources in utility computing environments, The Pro- ceedings of the European Conference on Computer Systems, Lisbon, Portugal, 289-302, 2007.

Kalyvianaki,E., Charalambous, T., Hand, S., Self-adaptive and self-configured CPU re- source provisioning for virtualized servers using Kalman filters, The Proceedings of the 6th International Conference on Autonomic Computing, New York, USA, 117-126, 2009.

Ercan, M., Acarman, T.,Iterative learning control of dynamic memory caching to enhance processing performance on Java platform, International Conference on Computational Sci- ence, Amsterdam, The Netherlands, 2664-2669, 2010.

Butcher,M., Karimi,A., Longchamp, R.,A statistical analysis of certain iterative learning control algorithms, International Journal of Control, 81, 156-166, 2008. http://dx.doi.org/10.1080/00207170701484851

Moon, J., Doh, T., Chung, M.J., An iterative learning control scheme for manipulators, Intelligent Robots and Systems, 2,759-765, 1997.

Yi, W., Zhongsheng, H., Xingyi, L., A novel automatic train operation algorithm based on iterative learning control theory, Service Operations and Logistics and Informatics, 2,1766- 1770, 2008.

Mi, C., Lin, H., Zhang, Y.,Iterative learning control of antilock braking of electric and hybrid vehicles, IEEE Transactions on Vehicular Technology, 54,(2), 486-494, 2005. http://dx.doi.org/10.1109/TVT.2004.841552

Xu, J., Wang, D., Wang, X.,The analysis of convergence speed for an open and closed loop second order iterative learning control algorithm, Intelligent Control and Automation, 1,3905-3909, 2006.

Xu, J.X., Yan,R.,On initial conditions in iterative learning control, Automatic Control, 50,(9), 1349-1354, 2005. http://dx.doi.org/10.1109/TAC.2005.854613

Abd-El-Barr, M., El-Rewini, H., Fundamentals of computer organization and architecture. New York: Wiley-Interscience, 2005.

Pitman, J., Probability. New York: Springer, 1999.

Ahn, H., Moore, K.L. Chen, Y., Iterative learning control: robustness and monotonic convergence for interval systems. New York: Springer, 2007. http://dx.doi.org/10.1007/978-1-84628-859-3

Booth, P., Chadburn, R., Haberman, S., James, D., Khorasanee, Z., Plumb, R.H., Rick- ayzen, B. Modern actuarial theory and practice (2nd ed.). Chapman & Hall/CRC, 2005.

Clarke, S., Baniassad, E., Aspect-oriented analysis and design: the theme approach. New Jersey: Pearson Education, 2005.

Published

2013-07-26

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