Date of Award

August 2013

Degree Type


Degree Name

Doctor of Philosophy


Management Science

First Advisor

Hemant K. Jain

Committee Members

Amit Bhatnagar, Sanjoy Ghose, Sanjeev Kumar, Huimin Zhao


Capacity Planning, Cloud Computing, Computer Simulation, Financial Risk, Is Development, Theoretical Modeling


Improvements in Information Technology (IT) infrastructure and standardization of interoperability standards among heterogeneous Information System (IS) applications have brought a paradigm shift in the way an IS application could be used and delivered. Not only an IS application can be built using standardized component but also parts of it can be hosted by different organizations in different locations provided it can be accessed using the Internet. This dissertation is an attempt to uncover unique aspects of this phenomenon known as Software as a Service (SaaS).

The first essay examines design decision making by SaaS providers by analyzing effects of two non-functional attributes of an IS Application - modularity and architectural performance. We model the relationship of the two attributes with factors such as demand, price, and user's preference. The model includes marginal cost and maintenance cost to recognize the service aspect of SaaS. Our results show the optimal values of various decision variables while taking into account user's sensitivity to modularity, architectural performance and price.

The service component in cloud computing necessitates that the service providers plan for requisite delivery capacity. The second essay addresses optimal infrastructure capacity planning while taking into account the opportunity cost of having low capacity and cost of unused capacity in the case of high capacity. We develop a model which provides insight to a SaaS provider on optimal capacity planning of IT infrastructure when faced with a variable demand and performance expectations.

The third essay focuses on financial risks faced by SaaS providers in the context of provider's risk tolerance. We analyze the financial risk of provider's decision making on pricing, capacity and other factors that may lead to financial risk as they are based on incomplete information. We built a model using Mean Variance Analysis theory for investigating the effect of provider's risk tolerance on infrastructure capacity planning while taking into account modularity in software architecture and operational performance.

This dissertation extends our understanding of significant issues facing a SaaS provider. The models presented here can form the basis for an extensive exploration of the phenomenon of SaaS specifically and Cloud Computing in general.