Analysis and Neural Networks Modeling of Web Server Performances Using MySQL and PostgreSQL
- 1 University of Fianarantsoa, Fianarantsoa, Madagascar
- 2 University of Fianarantsoa, Fianarantsoa, Madagascar
Abstract
The purpose of this study is to analyze and then model, using neural network models, the performance of the Web server in order to improve them. In our experiments, the parameters taken into account are the number of instances of clients simultaneously requesting the same Web page that contains the same SQL queries, the number of tables queried by the SQL, the number of records to be displayed on the requested Web pages, and the type of used database server. This work demonstrates the influences of these parameters on the results of Web server performance analyzes. For the MySQL database server, it has been observed that the mean response time of the Web server tends to become increasingly slow as the number of client connection occurrences as well as the number of records to display increases. For the PostgreSQL database server, the mean response time of the Web server does not change much, although there is an increase in the number of clients and/or size of information to be displayed on Web pages. Although it has been observed that the mean response time of the Web server is generally a little faster for the MySQL database server, it has been noted that this mean response time of the Web server is more stable for PostgreSQL database server.
- Rafamantanantsoa, F. (2009) Etude des performances d’un serveur web et d’un réseau local sans fil en utilisant les techniques neuronales. Université Blaise Pascal-Clermont-Ferrand II, Français, 121 p.
- Kamarudzzaman, K.A. (2009) Phase II: Comparison Report on MySQL and PostgreSQL. Malaysian Public Sector Open Source Software Programme, OSCC Report, 67 p.
- Gaspard, G., Jachniewicz, R., Lacava, J. and Meslard, V. (2009) équilibrage de Charge et Haute Disponibilité pour applications Web Ruby On Rails. LIP Asrall, 47 p.
- Shoaib, Y. and Das, O. (2011) Web Application Performance Modelling Using Layered Queuing Networks. Electronic Notes in Theoretical Computer Science, 275, 123-142. https://doi.org/10.1016/j.entcs.2011.09.009
- Waswani, V. (2005) PHP and PostgreSQL. Developer Shed, Melonfire, 20 p.
- Walker, J.D. and Chapra, S.C. (2014) A Client-Side Web Application for Interactive Environmental Simulation Modeling. Environmental Modelling & Software, 55, 49-60. https://doi.org/10.1016/j.envsoft.2014.01.023
- Mauchle, F. (2008) Database Replication with MySQL and PostgreSQL Software and Systems. University of Applied Sciences Rapperswil, Switzerland, 11 p.
- Conrad, T. (2004) Postgresql vs. Mysql vs. Commercial Databases: It Is All about What You Need. 5 p.
- Parizeau, M. (2012) Le perceptron multicouche et son algorithme de rétro propagation des erreurs. Département de génie électrique et de génie informatique, Université Laval, Québec, 8 p.
- Cornec, M. and Bertail, P. (2009) Validation Croisée et Modèles Statistiques Appliquées. Thèse Université Paris X, Nanterre, 47 p.
- Zell, A., Mamier, G., Vogt, M., Mache, N., Hübner, R., Döring, S., Herrmann, K.U., Soyez, T., Schmalzl, M., Sommer, T., Hatzgeorgiou, A., Posselt, D., Schreiner, T., Kett, B., Clemente, G., Wieland, J. and Gatter, J. (2008) Stuttgart Neural Network Simulator User Manual, Version 4.2. 350 p.
- Chiquet, J. Validation Croisée Pour le Choix de Paramètre de Méthodes de Régularisation. Module MPR—Option Modélisation, 8 p.
- Peña-Ortiz, R., Gil, J.A., Sahuquillo, J. and Pont, A. (2013) Analyzing Web Server Performance under Dynamic User Workloads. Computer Communications, 36, 386-395. https://doi.org/10.1016/j.comcom.2012.11.005
- Malrait, L., Bouchenak, S. and Marchand, N. (2009) Modélisation et contrôle d’un serveur. RenPar’/SympA’13/CFSE’’7, Toulouse, 12 p.