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. / February 2016

The aim of the work is to examine the issue of pricing network resources so as to ensure fair and efficient resource-sharing among users. The basic question we address is: Do there exist simple pricing schemes such that fair and efficient resource-sharing is ensured, even though (i) individ-ual users are concerned with maximizing their own net benefits, and (ii) a user alternates between data-limited and infinite-data phases? The Internet provides congestion control through the Transport Control Protocol TCP). TCP congestion control is dependent on voluntary participation of cooperative end users. If everyone uses TCP, congestion could be managed. However, it can be mathematically shown that it is economically more favorable for users to violate TCP rules. The published literature suggests pricing as a mechanism to control congestion. The context of operation is as follows. Each user is assumed to have a utility function which is a concave in-creasing function of the rate at which she sends data through the network. The problem is to find the vector of users' rates such that the sum of all users' utility functions is maximized, subject to resource capacity constraints. This constrained optimization problem can be solved in a central-ized manner if all the utility functions are known. In practice, however, the utility functions are not known and there is no central authority. In the literature this optimization problem has been decomposed into two sub-problems such that the knowledge of utility functions is not required. These problems are solved independently by the network and the users. It has been shown that at system optimum, the network computed vec-tor of rates and users’ choice of prices are in equilibrium and they also solve the system optimi-zation problem of maximizing sum of utilities of all users. But in all related work, the authors as-sume that users have infinite amount of data to sustain the system-optimal data rates indefinitely. In practice, however, users may run out of data at times. We propose a pricing scheme in which, under certain conditions, the following is possible. If some users run out of data, and hence are not able to inject traffic at their respective system op-timal data rates, it is possible for others with plenty of data to transmit above their system opti-mal rates. This allows efficient utilization of the resource at all times. Further, it is possible to compel users above optimal rates to back down when, at a later point of time, data-limited users are back with enough data. This ensures that fairness is maintained.

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