ABSTRACT Cyclic executives (CEs) offer the advantage of ensuring complete determinism with minimal runtime overhead, often making them the preferred choice for safety‐critical real‐time systems. However, generating CEs for multicore processors while addressing task precedence and exclusion relations presents significant challenges. In this paper, unlike previous work, we tackle these challenges by proposing integer linear programming (ILP) models to generate optimal preemptive and non‐preemptive CEs, considering both partitioned and global work allocation schemes. Additionally, we introduce a local search‐based heuristic to efficiently produce approximate solutions. Our methods are evaluated on both synthetic and benchmark instances from the literature, encompassing thousands of tasks and complex inter‐task dependencies, and include a direct comparison with a state‐of‐the‐art approximation method. The experimental results highlight the effectiveness of the proposed approaches in generating optimal or near‐optimal CEs for large‐scale task sets.

The clustering problem, or grouping, has two variants. If the number of clusters is predefined, this problem is known as the Clustering Problem (CP) or k-Clustering Problem, but when the number of clusters is not defined, the problem is known as the Automatic Clustering Problem (ACP). This paper proposes a new multi-population Biased Random-Key Genetic Algorithm (BRKGA) for the ACP, considering the silhouette index as similarity measure. In algorithm, several BRKGA populations evolve independently, such that each population is responsible for searching the best clustering for a given cluster number, i.e., each population solves one k-Clustering Problem for a particular k. Extensive experiments in 53 benchmark instances commonly used in the literature show that the algorithm obtained very competitive results compared to the state-of-the-art algorithms.

In this work, we conduct a comparative study of GPU accelerated metaheuristics for data clustering. Three population-based metaheuristics were implemented in GPU: Particle Swarm Optimization (PSO), Differential Evolution (DE), Scatter Search (SS). These metaheuristics were compared with the state-of-the-art methods for data clustering considering both runtime efficiency and solution quality. GPU-PSO and GPU-DE algorithms demonstrated competitive performance in the data sets proposed by the literature, as well as real-world problems. Moreover, experimental results show that our GPU proposal obtained an average speedup of 175x over the CPU-only implementation.

The amount of systems based on Internet of Things (IoT) has grown at an unprecedented rate over the last years and such an expansion tends to continue. As a consequence, billions of devices are expected to be deployed on diverse industries (e.g., healthcare, automotive) during the next decade. Due to its heterogeneity, the communication of IoT devices is a prominent system function and, thus, distinct communication protocols have been proposed for these systems. This paper presents a performance evaluation of IoT communication protocols for the application layer: AMQP, CoAP, and MQTT. Throughput, message size, and packet loss are the adopted metrics and experiments indicate CoAP protocol provides the best results.

Summary Cyber‐Physical Systems (CPSs) represent a new generation of smart systems that orchestrates physical elements with computation. This new class of system is intelligent and connected and is changing the way people deal with engineered systems, just as the Internet transformed the way people interact with information. Although several works have been proposed to support the design and development of CPSs, dependability evaluation of these systems have been investigated little. Dependability assessment (eg, reliability and availability) of cyber‐physical systems is of great importance as, very often, they are deployed in safety or business‐critical contexts. This paper presents a strategy based on Stochastic Petri Nets (SPNs) for dependability modeling, evaluation, and tuning of smart CPSs. The tuning is carried out through sensitivity analysis on the SPN models to efficiently identify the system components that most impact on the system's overall availability. The feasibility of our approach is demonstrated by evaluating a smart CPS deployed in a water treatment plant. Experimental results revealed that the proposed strategy helps highlight which components require attention when attempting to achieve high availability, and by adding redundancy to these components, the downtime of adopted CPS was reduced drastically from half a day to only 8 minutes.

Over the years, inbound logistics has been prominent to many industries, as globalization has forced companies to increase productivity while reducing costs. Transport modes considerably impact the costs, and a proper evaluation should consider performance, failures as well as sustainability issues. This paper presents an approach based on stochastic Petri nets (SPN) for assessing different transport modes in inbound logistics for coffee industry taking into account performability and sustainability.

Measurement is an important activity whenever design embedded systems, as it provides means for platform characterization and selection as well as system validation and specification. The sample frequency is a key aspect for obtaining accurate and precise measures. Obtaining highly accurate and precise measures may require measurement systems able to gauge the system under measurement at very high sample rates. The sample rate and the sensing device of a measurement system are features that decisively affect its prices. Variability and noise are intrinsic phenomena to be handled, thus the adoption of robust and fast statistic methods are indispensable for accuracy, precision and may favour the cost reduction of measurement systems. This paper adopts a low cost measurement system for gauging execution time and energy consumption of embedded software applications. This platform allows designers to obtain accurate and precise estimates of software piece of codes as well as it is an attractive means for supporting embedded software code tuning.

In this work, the problem of design space exploration of soft real-time embedded systems is formulated as a stochastic simulation optimization problem. A novel multiobjective genetic algorithm is proposed to address this problem. In the proposed algorithm, design metrics such as price and size are optimized while deadline violations are minimized. Experimental results show the advantages of our approach over previous work that consider no deadline violation is tolerable.

Accelerating the time to market for new embedded real-time systems is an imperative strategy nowadays. However, releasing poor-quality designs to the market can be dangerous, since a missed deadline in hard real-time systems can be catastrophic. Thus, early detection of potential problems in these systems is mandatory, since it may reduce the risks of fault propagations from early specification to the final code. This paper presents Calau, an environment for modeling and analyzing embedded real-time systems. Calau supports the mapping process of SysML State Machine diagram into a Time Petri Net with Energy consumption in order to analyze timing and energy consumption requirements in the early phases of embedded real-time systems development life cycle. It allows designers who do not have expertise in formal model, to design and analyze embedded system specifications quantitatively and qualitatively. We show the applicability of our tool by detailing an example.

The scientific community has devoted attention to virtualization in hard real-time systems, motivating the development of many hierarchical scheduling techniques for dealing with stringent timing constraints for such systems. Most techniques are based on runtime scheduling methods and they provide important results concerning schedulability analysis. However, there are situations in which runtime methods may fail in finding a feasible schedule, and intertask relations as well as overheads are neglected by several approaches. This paper proposes a hierarchical pre-runtime scheduling for hard real-time systems taking into account intertask relations and overheads. A formal model based on time Petri net (TPN) is adopted to provide a basis for precise schedule generation.

In this paper we use the Activity diagram of the System Modeling Language (SysML) in combination with the new UML profile for Modeling and Analysis of Real-Time and Embedded systems (MARTE) in order to validate functional, timing and low power requirements in early phases of the embedded system development life-cycle. However, SysML lacks a formal semantics and hence it is not possible to apply, directly, mathematical techniques on SysML models for system validation. Thus, a novel approach for automatic translation of SysML Activity diagram into Time Petri Net with Energy constraints (ETPN) is proposed. In order to depict the practical usability of the proposed method, a case study is presented, namely, pulse-oximeter. Besides, the estimates obtained (execution time and energy consumption) from the model are 95% close to the respective measures obtained from the real hardware platform.

This paper presents a performance and energy consumption modeling technique for embedded systems. The proposed method adopts a formal model based on Coloured Petri Nets for modeling the functional behavior of processors and memory architectures at a high-level of abstraction. The applicability of the proposed method is illustrated by evaluating a set of applications and a general-purpose microcontroller. Experimental results demonstrate an average accuracy of 96\% in comparison with the respective measures acquired from the real hardware platform. Moreover, the high-level behavioral representation of platforms allows the rapid analysis of performance and energy consumption of complex systems.

Dynamic voltage scaling (DVS) has been largely adopted as an effective technology for reducing energy consumption in embedded systems. Since the usage of DVS may affect the timing constraints of a hard real-time system, over the last decade, several pre-runtime as well as runtime scheduling approaches have been developed to tackle such an issue. Nevertheless, both have drawbacks that can be mitigated using a joint approach. This paper proposes a hybrid DVS scheduling approach for energy-constrained hard real-time systems, taking into account overheads, precedence and exclusion relations. The proposed method adopts a formal model based on time Petri nets in order to provide feasible schedules that satisfy timing and energy constraints.