Abstract
Complex cyber-physical systems are more and more a set of components working tightly coupled, with little or no human intervention. Assessing the correctness of these systems by testing components individually, one-by-one, is obviously not sufficient, being required to also test and validate the overall system. KhronoSim is a modular and extensible platform for testing cyber-physical systems in closed-loop, which enables the integration of simulation models and platform emulators to build a closed loop test environment. This paper presents the emulator module of KhronoSim, developed to integrate the well-known QEMU emulator in the closed-loop testing platform. © 2018 IEEE.

Abstract
This chapter describes a software architecture for processing big-data analytics considering the complete compute continuum, from the edge to the cloud. The new generation of smart systems requires processing a vast amount of diverse information from distributed data sources. The software architecture presented in this chapter addresses two main challenges. On the one hand, a new elasticity concept enables smart systems to satisfy the performance requirements of extreme-scale analytics workloads. By extending the elasticity concept (known at cloud side) across the compute continuum in a fog computing environment, combined with the usage of advanced heterogeneous hardware architectures at the edge side, the capabilities of the extreme-scale analytics can significantly increase, integrating both responsive data-in-motion and latent data-at-rest analytics into a single solution. On the other hand, the software architecture also focuses on the fulfilment of the non-functional properties inherited from smart systems, such as real-time, energy-efficiency, communication quality and security, that are of paramount importance for many application domains such as smart cities, smart mobility and smart manufacturing. © The Author(s) 2022. All rights reserved.

Abstract
Smart systems increasingly demand the processing of a massive amount of data generated by heterogeneous and distributed data sources. Due to the inherent cyber-physical nature of these systems, many applications require that this processing respects a set of non-functional requirements (such as timeliness, or energy-efficiency). To cope with this challenge, edge-cloud architectures need to provide flexible mechanisms to support varying processing needs, whilst guaranteeing the minimum level of quality of service required by these smart applications. This paper addresses this challenge in the context of the ELASTIC software architecture, which has been developed integrating responsive data-in-motion (edge computing) and latent data-at-rest analytics (cloud computing) into a single solution, satisfying extreme-scale analytics' performance requirements. The paper focuses on how the architecture fulfils the non-functional properties inherited from the applications, namely real-time and energy-efficiency, whilst ensuring the performance of the software architecture. © 2022 IEEE.

Abstract
The new generation of smart systems require processing a vast amount of information from distributed data sources, while fulfilling non-functional properties related to real-time, energy-efficiency, communication quality and security. The ELASTIC software architecture is being developed to tackle this challenge, considering the complete continuum from the edge to the cloud. This paper provides a brief analysis of the smart application considered in the project, and the requirements emanating from their non-functional properties. The paper then identifies some of the technical constrains imposed to the ELASTIC software architecture to allow guaranteeing the non-functional requirements of the systems.

Abstract
The ELASTIC European project addresses the emergence of extreme-scale analytics, providing a software architecture with a new elasticity concept, intended to support smart cyber-physical systems with performance requirements from extreme-scale analytics workloads. One of the main challenges being tackled by ELASTIC is the necessity to simultaneously fulfil the non-functional properties inherited from smart systems, such as real-time, energy efficiency, communication quality or security. This paper presents how the ELASTIC architecture monitors and manages such non-functional requirements, working in close collaboration with the component responsible for the orchestration of elasticity. © 2022, Ada-Europe. All rights reserved.

Abstract
The automotive software industry is gradually introducing new functionalities and technologies that increase the efficiency, safety, and comfort of vehicles. These functionalities are quickly accepted by consumers; however, the consequences of this evolution are twofold. First, developing correct systems that integrate more applications and hardware is becoming more complex. To cope with this, new standards (such as Adaptive AUTOSAR) and frameworks (such as AMALTHEA) are being proposed, to assist the development of flexible systems based on high-performance electronic control units (ECU). Second, the increase of functionality is supported by a dramatic increase of electronic parts on automotive systems. Consequently, the impact of software on the electrical power and energy non-functional requirements of automotive systems has come under focus. In this paper we propose an automatic and self-contained approach that supplements a model of an automotive system described on the AMALTHEA platform with energy-related annotations. From the analysis of simulation (or execution) traces of the modelled software, we estimate the power consumption for each software component, on a target hardware platform. This method enables energy analysis during the entire development life-cycle; furthermore, it contributes for the development of energy management strategies for dynamic and self-adaptive systems.