Business Models for Cloud Computing: Experiences from Developing Modeling & Simulation as a Service Applications in Industry

The potential of cloud computing is gaining significant interest in Modeling & Simulation (M&S). The underlying concept of using computing power as a utility is very attractive to users that can access state-of-the-art hardware and software without capital investment. Moreover, the cloud computing characteristics of rapid elasticity and the ability to scale up or down according to workload make it very attractive to numerous applications including M&S. Research and development work typically focuses on the implementation of cloud-based systems supporting M&S as a Service (MSaaS). Such systems are typically composed of a supply chain of technology services. How is the payment collected from the end-user and distributed to the stakeholders in the supply chain? We discuss the business aspects of developing a cloud platform for various M&S applications. Business models from the perspectives of the stakeholders involved in providing and using MSaaS and cloud computing are investigated and presented.

Management and engineering education in the 1950s and 1960s

The career destiny of many graduate engineers is in industry. Quite how well prepared they were for that career became a matter of political importance in the late 1950s and early 1960s as the issue of Britain's relatively poor economic performance grew in salience. This article looks at this preparation with particular reference to management training. It examines the attitudes of those parties most interested in engineering education ‐ the government, industry, the engineering institutions and the educators themselves. All of these saw management training as being part of the formation of at least a portion of professional engineers at some stage in their career. But there was no general agreement between them about what this should consist of or when it should be provided. At the same time broader changes in engineering education were taking place which cut across and to some extent militated against attempts to enhance the role of management training. The result, it is argued, is that by the end of the 1960s little progress had been achieved.

A multi-agent-based evolution model of innovation networks in dynamic environments

An innovation network can be considered as a complex adaptive system with evolution affected by dynamic environments. This paper establishes a multi-agent-based evolution model of innovation networks under dynamic settings through computational and logical modeling, and a multi-agent system paradigm. This evolution model is composed of several sub-models of agents' knowledge production by independent innovations in dynamic situations, knowledge learning by cooperative innovations covering agents' heterogeneities, decision-making for innovation selections, and knowledge update considering decay factors. On the basis of above-mentioned sub-models, an evolution rule for multi-agent based innovation network system is given. The proposed evolution model can be utilized to simulate and analyze different scenarios of innovation networks in various dynamic environments and support decision-making for innovation network optimization.