Abstract
Recently, PaaS (Platform as a Service) type cloud services are widely accepted as platforms for various web applications. Google App engine (GAE) is one of the most popular ones of such services. However, as for mission critical applications, there are several obstacles to migrate into these cloud services like GAE. One of the crucial obstacles is that, while such applications require predictable stable response time, it is difficult to predicate or estimate it in these services, since only a little performance information on these cloud services is available. In addition, the structure of them is not opened to general public. Therefore, it seems difficult to build a performance estimation model based on the system structure. This paper proposes a Colored Petri Net (CPN) based performance prediction model or framework for GAE, 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 delays. These delays are modeled using the queuing theory which is embedded in the CPN model. The framework has high modularity to plug-in any kinds of applications easily.
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