Customers of today's complex embedded systems demand the optimization of multiple system qualities under varying operational conditions. To be able to influence the system qualities, the system must have parameters that can be adapted. Constraints may be defined on the value of these parameters. Optimizing multiple system qualities under the given set of parameters and constraints is called Multi-Objective Optimization (MOO). This is a well-known mathematical problem, for which numerous solutions have been proposed. The application of an MOO solution in an embedded system involves specific design decisions. It is preferable that these design decisions are documented in the architectural description. Therefore, this paper presents an architectural style, which specializes the Component-and-Connector viewtype, to enable the analysis and design of an architecture from an MOO point of view. A case study from industry is used to demonstrate the usage of this style.
Control logic of embedded systems is nowadays largely implemented in software. Such control software implements, among others, models of physical characteristics, like heat exchange among system components. Due to evolution of system properties and increasing complexity, faults can be left undetected in these models. Therefore, their accuracy must be verified at runtime.Traditional runtime verification techniques that are based on states and/or events in software execution are inadequate in this case. The behavior suggested by models of physical characteristics cannot be mapped to behavioral properties of software. Moreover, implementation in a general-purpose programming language makes these models hard to locate and verify. This paper presents a novel approach to explicitly specify models of physical characteristics using a domain-specific language, to define monitors for inconsistencies by detecting and exploiting redundancy in these models, and to realize these monitors using an aspect-oriented approach. The approach is applied to two industrial case studies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.