As small and medium enterprises move toward cloud computing for their IT operations, they discover that securing their information assets is a challenge. A new set of providers offering fee-based security as a service (SECaaS) has emerged. The SECaaS market is still nascent, and important questions remain if these providers are to be successful. This research uses a system dynamics approach to shed light on the SECaaS market in the context of SECaaS costs, and security threat conditions. The results indicate that consumers are price sensitive but not threat sensitive, and have important implications for SECaaS providers and consumers.

The cloud computing industry is characterized by a small number of dominant providers and a host of small-to-medium providers. As the demand grows, additional providers are expected to enter the market. The need for 24/7 support and competitive pricing allows established providers to maintain defensible industry positions. This paper examines the impact of adopting growth and equilibrium market share strategies by cloud providers. Capacity planning remains a challenging problem given uncertain growth and demand, and providers seeking to pursue different market share strategies must adopt different tactics. Results from a system dynamics simulation indicate that market share growth can be achieved at the cost of overall revenue. A reactive strategy to maintain market share requires more price manipulation, but results in higher overall income. Simulations are conducted for a variety of economic conditions, with a view to providing cloud service providers broad guidance for cloud capacity building strategies.

In recent years, cloud computing has become a popular computing architecture in IT industry. Although information systems research has identified cloud computing as an important driver for cost saving and agility, there is little work that examines the implication of different innovation characteristics on cloud solution quality and eventual cloud diffusion. This research investigates the relationship between several innovation characteristics and the rate of diffusion on cloud computing within the industry. A blended approach is adopted, wherein the decision to adopt is modeled through agent-based simulation, and the characteristics of the innovation are varied through the use of genetic algorithms. Results indicate that the blended approach yields good results under a variety of innovation diffusion scenarios.

It is suggested that as rule-based system (RBS) technology gains wider acceptance, the need to create and maintain large knowledge bases will assume greater importance. Demonstrating a rule base to be free from error remains one of the obstacles to the adoption of this technology. An approach to RBS verification in which the system is modeled as a Petri net on which error detection is performed is presented. A set of propositions is formulated to locate errors of redundancy, conflict, circularity, and gaps in domain knowledge. Rigorous proofs of these propositions are provided. Difficulties in implementing a Petri net-based verifier and the potential restrictions of the applicability of this approach are discussed.>