Processing Capacity and Response Time Enhancement by Using Iterative Learning Approach with an Application to Insurance Policy Server Operation
Keywords:
iterative learning, caching, computational cost modelAbstract
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.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
Issue
Section
License
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
