Abstract
Developing distributed and scalable Cyber-Physical Systems (CPS) that can handle large amounts of data at high data rates at the edge, remains a challenging task. Also, the limited availability of open-source solutions makes it difficult for developers and researchers to experiment with and deploy CPSs on a larger scale. This work introduces Edge4CPS, an open-source multi-architecture solution built over Kubernetes that aims to enable an easy to use, efficient and scalable solution for the deployment of applications on edge-like distributed computing clusters. To verify the successful real-world implementation of the introduced architecture, the system was tested in a railway scenario, derived from the Ferrovia 4.0 project, which highlights its functionalities.

Abstract
High-performance cyber-physical applications impose several requirements with respect to performance, functional correctness and non-functional aspects. Nowadays, the design of these systems usually follows a model-driven approach, where models generate executable applications, usually with an automated approach. As these applications might execute in different parallel environments, their behavior becomes very hard to predict, and making the verification of non-functional requirements complicated. In this regard, it is crucial to analyse and understand the impact that the mapping and scheduling of computation have on the real-time response of the applications. In fact, different strategies in these steps of the parallel orchestration may produce significantly different interference, leading to different timing behaviour. Tuning the application parameters and the system configuration proves to be one of the most fitting solutions. The design space can however be very cumbersome for a developer to test manually all combinations of application and system configurations. This paper presents a methodology and a toolset to profile, analyse, and configure the timing behaviour of highperformance cyber-physical applications and the target platforms. The methodology leverages on the possibility of generating a task dependency graph representing the parallel computation to evaluate, through measurements, different mapping configurations and select the one that minimizes response time.

Abstract
Designing cost-efficient multi-core real-time systems requires efficient techniques to allocate applications to cores while satisfying their timing constraints. However, existing approaches typically allocate using a First-Fit algorithm, which does not consider the execution time and potential parallelism of paths in the applications, resulting in over-dimensioned systems. This work addresses this problem by proposing a new heuristic algorithm, Critical-Path-First, for the allocation of real-time streaming applications modeled as dataflow graphs on 2D mesh multi-core processors. The main criteria of the algorithm is to allocate paths that have the highest impact on the execution time of the application first. It is also able to exploit parallelism in the application by allocating parallel paths on different cores. Experimental evaluation shows that the proposed heuristic improves the resource utilization by allocating up to 7% more applications and it minimizes the average end-to-end worst-case response time of the allocated applications by up to 31%.

Abstract
Programmers resort to user-level parallel frameworks in order to exploit the parallelism provided by multiprocessor platforms. While such general frameworks do not support the stringent timing requirements of real-time systems, they offer a useful model of computation based on the standard fork/join, for which the analysis of timing properties makes sense. Very few works analyse the schedulability of synchronous parallel real-time tasks, which is a generalisation of the standard fork/join model. This paper proposes to narrow the gap by presenting a model that analyses the response-time of synchronous parallel real-time tasks. The model under consideration targets tasks with fixed priorities, composed of several segments with an arbitrary number of parallel and independent units of execution. We contribute to the state-of-the-art by analysing the response-time behaviour of synchronous parallel tasks. To accomplish this, we take into account concepts previously proposed in the literature and define new concepts such as carry-out decomposition and sliding window technique in order to compute the worst-case workload in a window of interest. Results show that the proposed approach is significantly better than current approaches, improving the state-of-the-art analysis of parallel real-time tasks. Copyright © 2014 ACM.

Abstract
Current generation of computing platforms is embracing multi-core and many-core processors to improve the overall performance of the system, meeting at the same time the stringent energy budgets requested by the market. Parallel programming languages are nowadays paramount to extracting the tremendous potential offered by these platforms: parallel computing is no longer a niche in the high performance computing (HPC) field, but an essential ingredient in all domains of computer science. The advent of next-generation many-core embedded platforms has the chance of intercepting a converging need for predictable high-performance coming from both the High-Performance Computing (HPC) and Embedded Computing (EC) domains. On one side, new kinds of HPC applications are being required by markets needing huge amounts of information to be processed within a bounded amount of time. On the other side, EC systems are increasingly concerned with providing higher performance in real-time, challenging the performance capabilities of current architectures. This converging demand raises the problem about how to guarantee timing requirements in presence of parallel execution. The paper presents how the time-criticality and parallelisation challenges are addressed by merging techniques coming from both HPC and EC domains, and provides an overview of the proposed framework to achieve these objectives.

Abstract
There is an increasing eagerness to deploy and execute parallel applications on many-core infrastructures, preserving the time-predictability of the execution as required by real-time practices to upper-bound the response time of the embedded application. In this context, the paper discusses the application of the currently-available WCET analysis techniques and tools on such platforms and with highly parallel activities. After discussing the pros and cons of all different methodologies for WCET analysis, we introduce a new approach that is developed within the P-SOCRATES project.