CPN Based GAE Performance Prediction Framework
Sachi Nishida, Yoshiyuki Shinkawa · 2014
Google App Engine (GAE) is one of the most popular PAAS type cloud platform for database transaction systems. When we plan to run those systems on GAE, performance prediction is one of the obstacles, since only a little performance information on GAE is available. In addition, the structure of GAE is not opened to general public. This paper proposes a Colored Petri Net (CPN) based simulation framework, based on the performance parameters obtained through the measurement by user written programs. The framework is build focusing on the application structure, which consists of a series of GAE APIs, and GAE works as a mechanism to produce the probabilistic process delay. The framework has high modularity to plug-in any kinds of applications easily.