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.

Multi-access Edge Computing (MEC) is a key enabler of 5 G networks, supporting handling latency, bandwidth, and connectivity requirements. However, integrating MEC with Software-Defined Networking (SDN) introduces significant challenges in resource management, Quality of Service (QoS) assurance, and scalability. Existing performance evaluation methods do not meet the expectations of addressing the complexity and reliability needed for SDN-based MEC systems. This paper proposes a stochastic modeling framework based on Stochastic Petri Nets (SPN) to assess the performance of MEC-SDN architectures. The model captures workload dynamics, system utilization, and failure conditions, providing a comprehensive and scalable performance evaluation tool. The method is validated through a real-world Vehicle-to-Infrastructure (V2I) scenario deployed on a cluster of single-board computers. Experimental results demonstrate up to 99.85% system utilization, low bandwidth consumption, and model validation within a 95% confidence interval. These outcomes confirm the model's effectiveness in evaluating SDN-enabled MEC deployments' resource allocation and performance.

The management and operational cost of public street lighting systems have driven the development of control architectures based on Internet of Things (IoT) technologies. These systems incorporate remote and autonomous control capabilities, enabling more efficient and scalable lighting operation. Despite recent advances, formal methods for evaluating the performance and energy behavior of smart street lighting systems are often overlooked during the design phase. This paper proposes a modeling approach based on Stochastic Petri Nets (SPN) to analyze the performance and energy consumption of adaptive street lighting systems. The model captures system behavior through operational states, message exchanges, and transmission failures. Experimental data obtained from LoRa-based microcontrollers are used to parameterize and validate the model under different configurations. The response time of the system is estimated using stationary analysis, and a sensitivity analysis is conducted to quantify the influence of key operational parameters such as message size and transmission interval. Results indicate that the transmission interval is the most influential factor affecting system responsiveness, while message size has a limited impact. The proposed model provides a formal and reproducible method for analyzing smart lighting infrastructure prior to deployment.

The evolution of mobile technology from the fifth-generation (5G) radio access has amplified challenges. As new requirements emerge, there is a need for enhanced computational capacity for planning, operation, and availability posed by 5G. Multi-access Edge Computing (MEC) and Software-defined networks (SDN) are fundamental to addressing capacity planning and availability. This paper presents a hierarchical modeling approach using reliability block diagrams (RBD) and continuous-time Markov chains (CTMC) to estimate the MEC SDN-based availability and capacity-oriented availability (COA). The proposed models allow analytical evaluations for calculating dependability metrics, showing an availability increase from 97.33% to 99.58%, with only two clustered server nodes, effectively reducing annual downtime from 357.32 hours to 0.65 hours.

The ossification of Internet's architecture is widely recognized as an important problem and network virtualization is a remarkable approach to deal with it. In this context, the optimal allocation of substrate resources is a NP-hard problem and there are many works proposing efficient mapping of virtual networks (VN). Evaluation and analysis of throughput on VNs are also important, as it directly impacts the quality of service of virtualized networks. This paper proposes a technique based on stochastic Petri nets (SPN) and reliability block diagrams (RBD) to assess availability-oriented throughput in virtualized network environments, using the availability of each physical network component to estimate VN throughput. Experimental results demonstrate throughput degradation when the distance of physical nodes increases. This work also presents a fault tolerance technique that mitigates such a degradation.

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.

Network virtualization has been considered as a mainstay for the success of overcoming the Internet's ossification problem, as it provides many advantages including scalability, traffic isolation, infrastructure abstraction and the promise of efficient use of network resources. Over recent years, Internet Service Providers (ISPs) have been particularly interested in reducing energy consumption in their infrastructures due to growing energy costs and increased ecological awareness. In this paper, we propose a metaheuristic-based approach to virtual network embedding (VNE) considering dependability constraints, in which energy savings are introduced by reducing the number of activated nodes and links. Results demonstrate that the proposed algorithms perform better than other important approaches for the adopted scenarios. Also, the results present the trade-off between availability, energy consumption and cost.

The need for performance has motivated the increasing adoption of solid-state drives (SSD) both in personal computers and data centers. However, the high cost per gigabyte and reduced lifetime hinder the thorough replacement of hard disk drives (HDD) with SSDs. To mitigate these issues, several architectures have been conceived based on hybrid storage systems, but performance and dependability models have not been proposed to better assess such different architectures. This paper presents an approach based on stochastic models for performance and availability modeling of hybrid storage systems, using stochastic Petri nets (SPN) and reliability block diagrams (RBD). A case study based on OpenStack Swift platform is adopted to demonstrate the feasibility of the proposed approach.

Governments can take advantage of cloud computing to deal with the pressures as well as challenges to reduce costs and improve service performance. In this context, the joint evaluation of performance and dependability (i.e., Performability) is very important, and stochastic modeling provides a prominent mechanism for assessing government services in the cloud and evaluation of distinct scenarios. This work proposes an approach based on stochastic Petri nets (SPN) for performability evaluation of Taxpayer Assistance Center (TAC) in the cloud. Performability techniques are considered, as failures in the process are taken into account and the respective impact in performance is analyzed. Results are presented to demonstrate the feasibility of the proposed approach, and they indicate that failures should not be neglected.

Cloud computing is a model in which resources such as storage, applications, and networking infrastructures can be offered as services over the internet. Cloud applications are becoming even bigger and more complex with a high availability requirement. This paper presents a modeling strategy based on a hierarchical and heterogeneous modeling for cloud infrastructure planning. This modeling strategy allows the selection of cloud infrastructures according to dependability and cost requirements. Additionally, a stochastic model generator for cloud infrastructure planning provides automatic generation of dependability and cost models for representing cloud infrastructures. A case study based on Moodle hosted on a Eucalyptus platform is adopted to demonstrate the feasibility of the proposed solution (modeling strategy and tooling).

Cloud computing is a computational paradigm in which resources such as storage, applications and networking infrastructures can be offered as services over the internet. Due to dynamic and virtualized nature of cloud environments and diversity of client requests, providing the expected service quality while avoiding over-provisioning is not a simple task. To ensure that the provisioned service is acceptable, providers must exploit techniques and mechanisms that guarantee a minimum level of service quality. This performance evaluation of cloud infrastructures has been receiving considerable attention by providers as a prominent activity for improving service quality. This paper presents a modeling strategy for cloud infrastructure planning with different software and hardware configurations, according to performance and cost requirements. A case study based on Moodle hosted on Eucalyptus platform is adopted to demonstrate the feasibility of the proposed modeling strategy.

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.

Cloud computing is a model that resources such as storage, applications and networking infrastructures can be offered as services over the internet. Cloud applications are becoming ever larger and more complex with high availability requirement. This paper presents a modeling strategy based on a hierarchical and heterogeneous modeling for planning of cloud infrastructures. The modeling strategy allows the selection of cloud infrastructures according to dependability and cost requirements. A case study based on virtual learning environments hosted on Eucalyptus platform is adopted to demonstrate the feasibility of the proposed modeling strategy.

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.

Hundreds of natural disasters occur in many parts of the world every year, causing billions of dollars in damages. This fact contrasts with the high availability requirement of cloud computing systems, and, to protect such systems from unforeseen catastrophe, a recovery plan requires the utilization of different data centers located far enough apart. However, the time to migrate a VM from a data center to another increases due to distance. This work presents dependability models for evaluating distributed cloud computing systems deployed into multiple data centers considering disaster occurrence. Additionally, we present a case study which evaluates several scenarios with different VM migration times and distances between data centers.

Over the last years, the evaluation of cloud computing infrastructures has received considerable attention as a prominent activity for improving service quality as well as planning modifications in an existing infrastructure. This paper presents Stochastic Model Generator for Cloud Infrastructure Planning (SMG4CIP), a automatic generation system of stochastic models for cloud infrastructure planning. This system indicates feasible cloud infrastructures according to dependability and cost requirements. The proposed system adopts a technique based on GRASP metaheuristic in order to recommend optimized cloud infrastructures. Furthermore, SMG4CIP adopts stochastic models, such as Petri Net and Reliability Block Diagram, to evaluate dependability and cost metrics. A case study based on Electronic Funds Transfer (EFT) system is adopted to demonstrate the feasibility of the proposed system.

One of the key characteristics of virtual networks (VN) is their intrinsic dynamical aspects. VNs allow network operators to have on-demand negotiation of a variety of services. Risks are inherent to virtualized infrastructures since the underlying physical network components are failure-prone. Therefore evaluation and analysis of risks in VNs, from assessment of dependability attributes, are of paramount importance for accurate management and control tasks. This paper proposes and evaluates a method to assess dependability in virtualized network environments. Through the use of Reliability Block Diagram (RBD) and Stochastic Petri Nets (SPN), we provide a first look at risks in network virtualization by means of dependability modeling and analysis. Our methodology allows the networking research community to develop new heuristics when dealing with resource allocation in VNs, while taking into account dependability attributes.

The cloud computing paradigm provides virtualized computing resources as on-demand services. This paradigm allows companies to focus on their business issues rather than conceiving and managing complex infrastructures. As a consequence, performance evaluation of cloud computing infrastructures has been receiving considerable attention by cloud service providers as a prominent activity for improving service quality as well as planning modifications in the current infrastructure to support future demands. This paper focus on the capacity planning of processing and storage resources related to an Electronic Funds Transfer service hosted on the Amazon EC2 cloud infrastructure.

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.

Business service through the Internet, particularly the cloud computing paradigm, have demanded more computational resources from data centers to provide high-availability services. As consequence, issues such as operational costs and sustainability increase, mainly, due to the redundant infrastructures needed to support the data center operation. However, data center designers have slightly information on the sustainability impact, operational cost and availability of data center architectures. In this context, models are important to support data center designers to estimate the environmental impact, dependability as well as the cost associated to the infrastructure before implementing it. This paper presents a set of formal models for estimating sustainability impact and dependability metrics, supported by an integrated environment, namely, ASTRO.

The number and size of data centers have greatly increased in recent years, mainly, due to the explosive growth of Internet services, such as those based on cloud computing paradigm. Therefore, service providers have intensified efforts on improving the quality of services while minimizing the costs of data centers, for instance, those associated to out-of-services periods. In other words, robust data center infrastructures are necessary, such that dependability requirements are satisfied while simultaneously incurring costs as minimal as possible. This paper proposes an approach that combines reliability importance indexes with TCA (Total Cost of Acquisition) to evaluate data center power infrastructures. The proposed approach aims at indicating the benefits and drawbacks of different architectures for power infrastructures to guide the decision maker in evaluating the most appropriate design.

The growth of engineering disciplines, such as maintainability and reliability, have been motivated by several factors, which include the ever growing of system complexity as well as the high cost associated to failures repairing activities and productivity reduction. This work adopts a methodology, which includes a hybrid modeling technique that considers the advantages of both stochastic Petri nets (SPN) and reliability block diagrams (RBD) to evaluate maintenance policies with different service level agreement (SLA). Dependability, cost and sustainability are also prominent features considered in the proposed methodology.

The advent of cloud computing has demanded more computational resources from data centers in order to provide high-availability services required in this new paradigm. To support data center dependability evaluation, this paper presents a tool, namely, ASTRO, which adopts a hybrid modeling approach, that includes Reliability Block Diagrams (RBD), Stochastic Petri Nets (SPN) and Data Center High-Level models. In addition, this tool also provides a prominent functionality in which an evaluation result can be reused in other models. ASTRO Tool also permits a hierarchical modeling, supporting the piecewise approach proposed by the methodology in which the tool is inserted.

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.

Over the last years, the issue of reducing energy consumption in hard real-time embedded systems has received considerable attention from the scientific community, since responsiveness and low energy consumption are often conflicting requirements. In this context, several techniques have been proposed, mostly, adopting DVS (dynamic voltage scaling) for reducing energy consumption in conjunction with runtime scheduling policies for dealing with stringent timing constraints. Nevertheless, those approaches generally disregard inter-task relations and runtime overheads (e.g. voltage switching) as well as adopt specialized operating systems. This paper presents a software synthesis method for hard real-time systems with energy constraints, taking into account DVS, inter-task relations and runtime overheads. The proposed method adopts a formal model based on time Petri nets in order to provide a basis for precise pre-runtime schedule generation.

Over the last decade, energy consumption has received considerable attention from the scientific community, which has developed several techniques for increasing energy savings in embedded systems. Nevertheless, considering time-critical systems, the adoption of energy saving techniques,such as dynamic voltage scaling, considerably hardens the system design, since stringent timing constraints may be affected. This work presents an environment for measuring and scheduling hard real-time systems with energy constraints, providing a set of integrated tools for automatic assistance in these design activities. Besides, a formal model based on time Petri nets is adopted in order to provide a basis for precise pre-runtime schedule generation as well as to allow property analysis and verification.

DVS (dynamic voltage scaling) has been adopted as an effective technique for reducing energy consumption in mobile embedded systems. Since reducing CPU supply voltage affects maximum CPU frequency, several concerns (e.g. deadline missing) may arise when adopting DVS in systems that contain hard real-time constraints. Although several scheduling approaches have been developed to address voltage scaling in time-critical systems, inter-task relations, such as precedence and exclusion relations, have been neglected. This work presents a method for hard real-time systems scheduling with DVS considering precedence and exclusion relations. The proposed work adopts a formal model based on time Petri nets in order to find a feasible schedule using a pre-runtime approach that satisfies timing and energy constraints.

The context of this work is related to embedded hard real-time systems development, more specifically, in the software generation phase. Embedded software has become much harder to design caused by the diversity of requirements and high complexity. Correctness and timeliness verification is an issue to be concerned. Usually, complex embedded real-time systems rely on specialized operating system kernels. However, operating systems may introduce significant overheads in execution time as well as in memory requirement. Software synthesis might be an alternative approach to operating systems usage, since it can generate tailored code for satisfying functional, performance, and resource constraints, and automatically generate runtime support (scheduling, resource management, communication, etc.) customized for each particular specification. However, the dispatcher and timer interrupt handler overheads are often neglect in software synthesis research. This paper provides a formal approach for system's modeling, and such model is adopted for synthesizing a timely and predictable scheduled code taking into account dispatcher and interrupt handler overheads.

This paper presents a methodology for mapping the live sequence chart (LSC) language to an equivalent coloured Petri net (CPN) model LSC is a language for system specification, allowing one to specify what should happen for all execution of the system as well as the modeling of anti-scenarios. However, analysis and verification of system properties are not possible. In order to allow diagnosis of inconsistent specifications, besides simulation, verification and analysis should be considered. Therefore, the proposition of a CPN model for LSC allows verification and analysis of system described in LSC, hence, contributing for increasing designers' confidence on the system development process and reducing risk that may lead to project failure.

Embedded hard real-time systems have stringent timing constraints that must be satisfied for the correct functioning of the system. Hence all tasks must be finished before their deadlines. In addition, there are systems where energy is another constraint that must also be satisfied. In this paper, a pre-runtime scheduling algorithm is presented in order to find schedules satisfying both timing and energy constraints. The proposed approach uses state space exploration for finding pre-runtime schedules. However, the main problem with such methods is the space size, which can exponentially grow. This paper tackles this problem through a depth-first search method for generating a partial timed labeled transition system derived from the time Petri net model.