Systems Modeling Language (SysML) is an object management group standard for systems-of-systems engineering. It enables the description of complex system models; however, it cannot effectively support all system engineering activities. For instance, system performance evaluation is usually performed via simulation. In this case, the transformation of SysML system models to executable simulation models for specific simulation methodologies and tools is required. Model transformation is a key component for addressing the challenges of seamless integration of SysML model simulation in model-based system engineering. In this paper, we explore a declarative approach, based on the query/view/transformation-relations (QVT-R) standard, for the transformation of SysML models to executable simulation models, fully adhering model-driven architecture (MDA) concepts. It is supported by a framework implemented to provide executable simulation model and code generation. Methodological guidelines, for the effective use of a declarative language as QVT-R, for model transformation, are provided, emphasizing the utilization of existing domain-specific SysML profiles, as well as executable simulation library components. The experience obtained from two different domains, namely, enterprise information and railway transportation systems, modeled as systems-of-systems via SysML, is discussed, based on a quantitative analysis of the respective QVT-R transformations.

Model-based systems design (MBSD), a current trend adopted by INCOSE, employs the systems modeling language (SysML), a standard introduced by OMG and INCOSE. Though there are numerous works on integrating performance exploitation in SysML, cost is not sufficiently explored as a driving design parameter. By integrating cost analysis in a popular modeling language like SysML, the proposed approach may be applied to any system designed using standardized languages and tools. In this work, we integrate cost analysis within SysML models at a generic level to explore design alternatives under specific cost and performance restrictions and perform tradeoff analysis. The proposed SysML extensions provide: 1) cost-related entities to encode cost aspects, such as capital and operating expenses, and 2) functions that enable the automatic computation of costs; these extensions are contained in a custom SysML cost profile. The feasibility and benefits of the approach are explored in two distinct real-world case studies with different purpose and characteristics. 1) Configuring a remote elderly monitoring system, taking into consideration patients' budgetary and operational concerns regarding the equipment installed in their homes. In this case, patients had the opportunity to evaluate and prioritize their concerns prior to using the system. 2) Exploring the improvement of the passengers' comfort as a level of service indicator in the Athens Metro railway system, taking into account operational cost constraints. In this case, the operator obtained forecasts of performance and cost of the metro system operation in order to choose between different operational policies.

To maximize the business value of the cloud, the cost of cloud solutions is explored alongside technical quality and performance. To enable this form of exploration engineering, finance and business teams collaborate in the context of FinOps, the operational framework that provides the required decision-making. Prominent providers, such as Google and Microsoft, provide FinOps to their customers, integrating cost factors when designing a cloud solution. However, different providers apply different pricing policies for their products, and these policies also change through time. Therefore, there are numerous efforts to explore price evolution through time for different cloud products applying different decision-making methods using different datasets. In an effort to establish a systematic approach to support decision-making on alternative pricing policies for cloud services and compare them across services and providers, the CloudPricingOps framework is proposed in this paper. It constitutes a decision support system that provides alternative decision-making methods, such as hedonic models, time series, and clustering and machine learning techniques, to deal with problems related to cloud product pricing policy analysis, comparison and prediction. It also constitutes a systematic method of discrete steps to integrate additional decision-making methods to deal with these problems and input datasets to be used regardless of the method or the cloud pricing problem that needs to be solved. Two discrete examples based on real data are also presented in this paper to demonstrate the usefulness of the CloudPricingOps framework for cloud engineering and business teams.

There are numerous enterprise information system (EIS) engineering methodologies in the literature, each covering different aspects. However, in order to integrate them in an enterprise architecture, model-based engineering can be adopted. In such a case, a central system model is defined supporting all engineering activities. Zachman's matrix may be used as a basis for constructing such a model. Based on this assumption, we propose a systematic approach for the support of model-based EIS engineering process using Zachman matrix as EIS central model. Basic EIS engineering activities and the way they may be served by specific rows is explored, while the contribution of each system aspect (matrix column) is also taken into account. A conceptual model for model-based EIS engineering is also introduced. To explore the proposed concepts in practice, the system network cell is used as an example. Corresponding engineering tasks and sub-models are formed based on the proposed guidelines in a technology and methodology independent fashion. A case study based on the proposed concepts is also presented.