SELF ADAPTIVE SOFTWARE DENGAN TEKNIK CASE-BASED REASONING UNTUK MENINGKATKAN PERFORMA APPLICATION SERVER

Article Sidebar

PDF
Published: Oct 30, 2018
DOI: https://doi.org/10.33322/kilat.v7i2.355
Keywords:
Self adaptive Software, Self-configuring, CBR, Framework, Application server

Main Article Content

Muhamad Nur
Domo Pranowo Kuswandono

Abstract

Performance of application server is an important attribute that must be maintained quality and stability, because application server operated in dynamic environment with fluctuating user loads and resource levels, its potentially caused unpredictable errors. In order application server performance still optimal, it is need maintains configuration application parameters. But, do that process need deep observation each environment condition changed and of course the cost and time. The approach that can be used is self-adaptive (autonomic computing). Self-adaptive can configure application parameters automatically. Best combination application parameters configuration can be stored on a repository and used as reference when decide new decision for configuration of application server parameters when similar condition is occurred. Cased-based reasoning can do the process. Best approach will be used is self-adaptive with case-based reasoning. This research implements the approach on an experiment application that deployed on glassfish application server. The results show that self-adaptive with case-based reasoning can improve application server performance with significant improvement.

Downloads

Download data is not yet available.

Article Details

How to Cite
Nur, M., & Kuswandono, D. P. (2018). SELF ADAPTIVE SOFTWARE DENGAN TEKNIK CASE-BASED REASONING UNTUK MENINGKATKAN PERFORMA APPLICATION SERVER. KILAT, 7(2), 120–131. https://doi.org/10.33322/kilat.v7i2.355
Issue
Vol. 7 No. 2 (2018): KILAT
Section
Articles

References

[1] Dobson, S., Denazis, S., Fernandez, A., & Gaiti, D. (2006). A Survey of Autonomic Communications. ACM Transaction Autonomic Adaptation System, 1, 2, 223-259.
[2] Fayad, M., & Schmidt, D. C. (1997). Object-Oriented Application Frameworks. Special Issue on Object-Oriented Application Frameworks, 1.
[3] Gorton, I., Liu, A., & Brebner, P. (2003). Rigorous Evaluation Of COTS Middleware Technology. IEEE Computer Society.
[4] Jordan, M., Czajkowski, G., Kouklinski, K., & Skinner, G. (2004). Extending a J2EE Server with Dynamic and Flexible Resource Management. Association for Computing Machinery.
[5] Kephart, J. O., & Chess, D. M. (2003). The Vision Computer. IEEE Computer Society.
[6] Khan, M. J., Awais, M. M., & Shamail, S. (2008). Enabling Self-Configuration in Autonomic Systems using Case-Based Reasoning with Improved Efficiency. Paksitan: IEEE Computer Society.
[7] Kolodner, J. L. (1992). An Introduction to Case-Based Reasoning. Atlanta: College of Computing.
[8] Kusrini, & Luthfi, T. E. (2009). Algoritma Data Mining. Yogyakarta: Andi.
[9] Laddaga, R. (2000). Active software. Proceedings of the International Workshop on Self-Adaptive, 11-26.
[10] Laddaga, R. (1999). Creating Robust Software through Self-Adaptation. IEEE Computer Society.
[11] Liu, Y., & Gorton, I. (2007). Implementing Adaptive Performance Management in Server Applications. Chicago: IEEE Computer Society.