Special Section Introduction: Cloud Computing as a Service

Cloud computing is a new way of delivering computing services. The flexibility, scalability, and cost effectiveness of cloud computing provides tremendous business opportunities to customers. The elated customer expectation, however, poses daunting challenges to cloud service providers, whether they are traditional data centers or fresh players in the arena. These practical challenges raise new research questions to communities, both computer science oriented and service science oriented. Performance analysis and service management are required to be knitted together for the success of the end game. This special section aims to facilitate the development of new theories as well as their permeation to the practice fields. We provide a platform for both researchers and practitioners to communicate their new ideas so that important problems in cloud computing services can be addressed from a more complete point of view. Various aspects of cloud computing service have been discussed in the set of articles presented here. In particular, we are pleased that several studies involve the development of new methodology and concepts in fields such as statistics, computer science, operations research, and marketing science. The Iyoob et al. (2013) paper seeks to understand the role that operations research (OR) plays in cloud computing. The authors effectively organize their research questions into three areas according to host: (1) cloud providers, (2) cloud consumers, and (3) cloud brokers. They conducted a thorough survey on existing research in this area. The authors argue that cloud computing, as a new service form, could be a fresh force that stimulates new research in OR. More specifically, the authors identify several less developed and more challenging areas, such as pricing (from the provider’s perspective), intrusion detection and prevention (from the provider’s perspective), and capacity reverse auctions (from the broker’s perspective). The authors expect new and exciting research in these areas. In the paper by Bohling et al. (2013), the problem of cloud computing service adoption is viewed in a broad perspective of radical innovation adoption, an innovative addition to the fundamental research on relationship marketing. More specifically, several important decisions in adopting cloud computing services, such as purchase timing, product choice, and purchase amount, have been analyzed, and a new construct for explaining customer behavior has been added to the theory of the relationship marketing. Numerical experiments provide validation of the model, as well as interesting observations. The paper by Lee et al. (2013) showcases a recent application that integrates advanced energy analytics and cloud computing service. Sophisticated analytic tools have been developed for analyzing and controlling the building energy consumption. Energy consumption reduction can be maximized only when all the parties involved have access to the information and each contributes his or her part in the equation. The cloud service provides a venue for the parties to access the desired information. It enables building owners, facility managers, operators, and tenants to access necessary analytic tools with no need to install the tools themselves. In this application, cloud service is indeed a key enabler of the technology. The paper by Gaivoronski et al. (2013) introduces several portfolio optimization models from the perspective of cloud brokers. This study fills the gap between the technical aspect and the economic/financial aspect of cloud brokering. A set of models is developed to find the best portfolio of connectivity options that maximizes the expected profit of the cloud broker while satisfying a constraint on the quality of service (QoS). The novelty of these models lies in the fact that they not only incorporate fixed costs but also consider the uncertainty in demand and QoS. Theoretical analysis and business insights are delivered through numerical experiments, utilizing observations from real Internet traffic data.