TY - JOUR AU - Çapari, Klea AU - Elmazi, Donald AU - Prieditis, Marcis PY - 2022/10/31 Y2 - 2024/03/28 TI - Efficiency Performance Evaluation on Multi-user Web Application Platforms in Cloud Computing JF - International Journal of Innovative Technology and Interdisciplinary Sciences JA - IJITIS VL - 5 IS - 3 SE - Articles DO - 10.15157/IJITIS.2022.5.3.1014-1032 UR - https://journals.tultech.eu/index.php/ijitis/article/view/96 SP - 1014-1032 AB - <p>Cloud computing is a well-known paradigm nowadays because it decreases the cost to access the application, for a massive amount of data from anywhere in the world via internet. This paper takes the approach of testing the performance of web application deployment environment. The main objective of this paper was to investigate the performance of web application deployment infrastructure by growing eventually the number of users that visit the web application concurrently. The infrastructure that was used is part of the services provided by cloud computing, more specifically Platform as a Service (PaaS). This service provided a runtime environment in which we easily created, tested and deployed the web application. Tests were designed by using an open source tool. Web application subject for testing purposes was an open source pet shop application which fulfils the criteria of being a multi-user web application. Tests were created by using an open source application called Apache JMeter. One of main goals was to develop a proper test plan by considering user behaviour accessing a web application. We have developed and implemented three scenarios, started with deployment of the platform, installing dependencies and finally installing the web application used for performance testing. We have tested 2 different deployment platforms, in the first environment everything is installed in one machine and in second environment we separate application server from the database server. We have concluded in results where processes like register, login and checkout consumes much more resources of the server. In the future we will try to understand where machine learning stands in this part of web application development and how it can affect deployment infrastructure.</p> ER -