Abstract
More and more cyber-physical systems and the internet of things push for a multitude of devices and systems, which need to work together to provide the services as required by the users. Nevertheless, the speed of development and the heterogeneity of devices introduces considerable challenges in the development of such systems. This paper describes a solution being implemented in the setting of a serious game scenario, connected to real homes energy consumption. The solution provides a publish-subscribe middleware which is able to seamlessly connect all the components of the system.

Abstract

Abstract
Scheduling real-time applications on general purpose multicore platforms is a challenging problem from a timing analysis perspective. Such platforms expose uncontrolled sources of interference whenever concurrent accesses to memory are performed. The non-deterministic bus and memory access behavior complicates the estimations of applications' worst-case execution times (WCET). The 3-phase task model seems a good candidate to circumvent the uncontrolled sources of interference by isolating concurrent memory accesses. A task is divided in three successive phases; first, the task loads its instruction and data in a local memory, then it executes non-preemptively using those pre-loaded instructions and data, and finally, the modified data are pushed back to main memory. Following this execution model, tasks never access the bus during their execution phase. Instead, all the bus accesses are performed during the first and third phases. In this paper, we focus on the global fixed-priority scheduling of the 3-phase task model. A new schedulability test is derived by modelling the interference happening on the bus rather than the interference on the cores as in the state-ot-the-art techniques. The effectiveness of the test is evaluated by comparing it against the state-of-the-art.

Abstract
Future real-time embedded systems will increasingly incorporate mixed application models with timing constraints running on the same multi-core platform. These application models are dataflow applications with timing constraints and traditional real-time applications modelled as independent arbitrary-deadline tasks. These systems require guarantees that all running applications execute satisfying their timing constraints. Also, to be cost-efficient in terms of design, they require efficient mapping strategies that maximize the use of system resources to reduce the overall cost. This work proposes an approach to integrate mixed application models (dataflow and traditional real-time applications) with timing requirements on the same multi-core platform. It comprises three main algorithms: 1) Slack-Based Merging, 2) Timing Parameter Extraction, and 3) Communication-Aware Mapping. Together, these three algorithms play a part in allowing mapping and scheduling of mixed application models in embedded real-time systems. The complete approach and the three algorithms presented have been validated through proofs and experimental evaluation. © 2017 Copyright held by the owner/author(s).

Abstract
This paper extends the work presented in Maia et al. (Semi-partitioned scheduling of fork-join tasks using work-stealing, 2015) where we address the semi-partitioned scheduling of real-time fork-join tasks on multicore platforms. The proposed approach consists of two phases: an offline phase where we adopt a multi-frame task model to perform the task-to-core mapping so as to improve the schedulability and the performance of the system and an online phase where we use the work-stealing algorithm to exploit tasks' parallelism among cores with the aim of improving the system responsiveness. The objective of this work is twofold: (1) to provide an alternative scheduling technique that takes advantage of the semi-partitioned properties to accommodate fork-join tasks that cannot be scheduled in any pure partitioned environment and (2) to reduce the migration overheads which has been shown to be a traditional major source of non-determinism for global scheduling approaches. In this paper, we consider different allocation heuristics and we evaluate the behavior of two of them when they are integrated within our approach. The simulation results show an improvement up to 15% of the proposed heuristic over the state-of-the-art in terms of the average response time per task set.

Abstract
The original version of this article unfortunately contained an error in the author affiliation. The corresponding author, “Dr. Borislav Nikoli´c” is currently affiliated in “Technische Universität Braunschweig”, but the work of this paper was performed and funded by CISTER/INESC-TEC, ISEP, IPP. Therefore, the corresponding author is linked to both the affiliations. This has been corrected with this erratum.