An emerging trend in Internet of Things (IoT) applications is to move the computation (cyber) closer to the source of the data (physical). This paradigm is often referred to as edge computing . If edge resources are pooled together, they can be used as decentralized shared resources for IoT applications, providing increased capacity to scale up computations and minimize end-to-end latency. Managing applications on these edge resources is hard, however, due to their remote, distributed, and (possibly) dynamic nature, which necessitates autonomous management mechanisms that facilitate application deployment, failure avoidance, failure management, and incremental updates. To address these needs, we present CHARIOT, which is orchestration middleware capable of autonomously managing IoT systems consisting of edge resources and applications. CHARIOT implements a three-layer architecture. The topmost layer comprises a system description language, the middle layer comprises a persistent data storage layer and the corresponding schema to store system information, and the bottom layer comprises a management engine that uses information stored persistently to formulate constraints that encode system properties and requirements, thereby enabling the use of satisfiability modulo theory solvers to compute optimal system (re)configurations dynamically at runtime. This article describes the structure and functionality of CHARIOT and evaluates its efficacy as the basis for a smart parking system case study that uses sensors to manage parking spaces.

Wider adoption, availability and ubiquity of wireless networking technologies, integrated sensors, actuators, and edge computing devices is facilitating a paradigm shift by allowing us to transition from traditional statically configured vertical silos of Cyber-Physical Systems (CPS) to next generation CPS that are more open, dynamic and extensible. Fractionated spacecraft, smart cities computing architectures, Unmanned Aerial Vehicle (UAV) clusters are all examples of extensible CPS wherein extensibility is implied by the dynamic aggregation of physical resources, affect of physical dynamics on availability of computing resources, and various multi-domain applications hosted on these systems. However, realization of extensible CPS requires resolving design-time and runtime challenges emanating from properties specific to these systems. In this paper, we first describe different properties of extensible CPS - dynamism, extensibility, remote deployment, security, heterogeneity and resilience. Then we identify different design-time challenges stemming from heterogeneity and resilience requirements. We particularly focus on software heterogeneity arising from availability of various communication middleware. We then present appropriate solutions in the context of a novel domain specific language and describe how this language and its features have evolved from our past work.

Software systems are nowadays often configured by sales people, domain experts, or even customers instead of engineers. Configuration tools communicate the systems' variability to these end users and provide guidance for selecting and customizing the available features. However, even if a configuration tool creates technically correct systems, addressing the specific needs of business-oriented users remains challenging. We analyze existing configuration tools to identify key capabilities for guiding end users and discuss these capabilities using the cognitive dimensions of notations framework. We present an implementation of the capabilities in our configuration tool DOPLER CW. We performed a qualitative investigation on the usefulness of the tool's capabilities for user guidance in product configuration by involving nine business-oriented experts of two industry partners from the domain of industrial automation. We present key results and derive general implications for tool developers.

Industrial experiences show that support for sharing and deploying product line models and tools is essential when institutionalizing product line engineering. This paper presents key workflows together with an infrastructure providing support for this purpose. Our approach supports distributed users sharing work products during variability modeling, product derivation, and product line evolution. The approach is based on product line bundles (PLiBs) for packaging models and tool support for specific product lines. Using three industrial scenarios and an industrial product line example we demonstrate how our infrastructure supports the deployment of models and tools in practical settings.

Developers of software engineering tools are facing high expectations regarding capabilities and usability. Users expect tools tailored to their specific needs and integrated in their working environment. This increases tools' complexity and complicates their customization and deployment despite available mechanisms for adaptability and extensibility. A main challenge lies in understanding and managing the dependencies between different technical mechanisms for realizing tool variability. We report on industrial experiences of applying a model-based and tool-supported product line approach for the customization and deployment of two Eclipse-based tools. We illustrate challenges of customizing these tools to different development contexts: In the first case study we developed variability models of a product line tool suite used by an industry partner and utilized these models for tool customization and deployment. In the second case study we applied the same approach to a maintenance and setup tool of our industry partner. Our experiences suggest to design software tools as product lines; to formally describe the tools' variability in models; and to provide end-user capabilities for customizing and deploying the tools.