This dissertation consists of three main chapters that examine economic and operational issues in cloud computing market using analytical modeling. Chapter 2 studies the problem of selecting computing resources from a cloud provider with the goal to minimize the total rental cost of completing a computing task in the presence of a time constraint. The problem is formulated as a scheduling problem that assigns computing resources to time periods of the planning horizon (time available to complete a single computing task). This (NP-hard) preemptive-resume type scheduling problem – new to the scheduling literature – has not been carefully addressed in practice to provide an implementable solution. Typically, the approach taken in practice is to use a single resource (a single virtual machine instance, or a cluster of identical virtual machine instances) to complete a computing task. The main insight of this study is that rather than completing a computing task using a single computing resource, rental costs can be significantly lowered by using a few resources (sometimes, even just two) to complete the task. Thus, the computing task is switched from one resource to another to exploit the cloud provider’s price-performance schedule. Cloud computing has been recognized as an economically attractive computing environment whose adoption has been growing over time. However, providers (such as Amazon Web Services) offer a confusing and diverse set of computing resources with different configurations and unit rental costs. Our near-optimal solution is based on switching the computing task from one resource to another in way that leverages the relationship between the price and performance of the available computing resources. The performance of a given resource can vary randomly as well as be correlated with the performance of another (stronger or weaker) resource. We present a worst-case performance guarantee of the proposed solution. In addition, we study the performance using a detailed computational study and a real-world example of an actual company that can benefit from our proposed solution. In the computational study as well as the real-world example, the cost of our solution is usually about 15% – 25% lower than the benchmark solution of using the best single computing resource to process the computing task. Practicing IT managers can use our approach to migrate in-house solutions to the cloud in a cost-effective manner. In Chapter 3, we consider an ad-firm that acts on behalf of advertisers to execute mobile, in-app, ad campaigns. The firm commits to provide an advertiser a specified number of ad placements (impressions) on mobile apps, usually in a specified location, and within a specified time horizon. The supply for ad space arrives – in real-time – in the form of bid requests from one or more mobile ad-exchanges. The ad-firm needs to bid on each impression in such a way that the goals of several on-going campaigns are met at minimum cost. The ad-firm needs to execute multiple campaigns simultaneously and get its supply (for ad space, or impressions) from multiple mobile ad-exchanges. By working with more than one ad-exchange, the direct cost of procuring the necessary impressions can be lowered. However, this lower cost needs to be balanced with the cost of the additional computing resources needed to work with multiple mobile ad-exchanges, as well as the (possible) extra cost of meeting the minimum spend (or participation fee) imposed by each ad-exchange. In this study, we propose a cloud-based architecture to procure impressions for an ad-firm. There are two key decisions that the firm needs to make. First, it needs to select the set of mobile ad-exchanges to obtain its supply. Each mobile ad-exchange is characterized by specific supply uncertainties, location dependent win-curves, and a participation fee. Second, for each ad-exchange and location, the ad-firm needs to determine its bidding policy, i.e., how much to bid for each bid-request. We show that the proposed near-optimal bidding strategy – the strategy to bid at each exchange-location combination – is state independent. We first analyze a special case with identical mobile ad-exchanges and show that, depending on the particular parameter setting, the near-optimal number of ad-exchanges and the near optimal bid amount can be weak complements or substitutes. We next solve the general problem of selecting among multiple non-identical ad exchanges. Finally, we propose a supply flexibility architecture that can further lower procurement costs. The ideas of this paper are applied to a real problem and the savings from our approach (about 33% lower cost) are demonstrated. In Chapter 4, we analyze the webspeed competition between two electronic retailing firms. As of 2018 (2010), Google has been using a new ranking algorithm designed for mobile (desktop) search that uses webspeed (or equivalently, website response time) as a ranking factor. The emerge of Google Speed Update makes firms aware about a new possible decision process to manage the performance of their websites, namely, response-based. The response based decision process starts with choosing a desired level of webspeed (or equivalently, website response time) which in turn determines the cloud computing capacity level. Before Google Speed Update, firms were aware about capacity-based decision process that starts with choosing the cloud computing capacity level which in turn determines the website response time. We consider a duopoly where two electronic retailing firms compete for customer traffic from Google search result page and make capacity or website response time decisions in equilibrium. Our analysis shows that the emerge of Google Speed Update intensifies the competition. In other words, capacity-based competition generates higher profits for both firms than those generated in response-based competition. However, when the firms are free to choose the decision process, a response-based decision process is chosen by both firms in equilibrium. We recommend that managers of a focal firm should adopt a response-based decision process if the rival firm employs a capacity-based decision process.