Cloud computing at a high level comprises of the availability of hardware, software and technical support via a network protocol to a remote client on a pay-per-use basis. Businesses using Cloud resources has been increasing steadily in the very recent past and the number of Cloud service providers (CSP) are increasing as well. The challenges that characterize a Cloud data center include: on-demand service, elasticity, resources pooling, broad network access, service meters. As the customer base is in creasing and their resource requirement and usage pattern has been becoming highly volatile, proper utilization of the resources and generating revenue by appropriately charging the clients for their uses has become an even more challenging research problem. In other words, Cloud resource pricing has emerged as an important and pressing problem to study for ever increasing utility of Cloud computing. Literature review reveals that there are economy-based models (cash flow, net present value etc.) used for charging mechanism suggested by many researchers. Most of these models are rigid that they are not build with the core of Cloud - elasticity in mind. Also, the economic models do not provide flexibility of the economy of scale to either increase or decrease the resource requirement and appropriately charge for such increase or decrease in resource use. For my thesis, I have designed and developed a Cloud resources pricing model that satisfies two important constraints: the dynamic ability of the model to provide a high satisfaction guarantee measured as Quality of Service (QoS) - from users perspectives, and profitability constraints - from the Cloud service providers perspectives. I have employed financial option theory and treated the Cloud resources as underlying assets to capture the realistic value of the Cloud Compute Commodities (C3). I have priced the Cloud resources using my model. Through this research, I show that the Cloud parameters can be mapped to financial economic model and that this model can be effectively implemented for resource pricing purpose. I discuss the results of pricing Cloud Compute Commodities (C3) for various input parameters, such as the age of the resource and quality of service.