Unmanned Aerial Vehicles (UAVs) are increasingly used for road traffic monitoring due to their mobility and wide-area coverage. However, their limited onboard resources make real-time video analysis challenging under dynamic traffic conditions. To overcome this, computational task offloading to nearby fog nodes is often employed. The main challenge lies in deciding when to process locally or offload, as both traffic and computational load vary continuously. Existing heuristic-based approaches are lightweight but rely on fixed thresholds, leading to unstable switching and degraded performance under fluctuating conditions. Meanwhile, Deep Reinforcement Learning (DRL)–based methods can adaptively optimize offloading but require extensive training and high computational costs, limiting their practicality on UAVs. To address this challenge, we propose Dynamic Vehicle Density-aware Offloading (DVDOffload), an adaptive task offloading technique designed to maximize performance and resource efficiency by adapting the offloading decision to road traffic conditions. The proposed method uses vehicle density as the primary workload indicator and dynamically adjusts offloading thresholds using an Exponential Moving Average (EMA) to ensure adaptive and stable decisions. Experimental results in multiple realistic traffic scenarios show that DVDOffload achieves higher accuracy, faster processing, and lower resource consumption compared to several baseline heuristic and DRL-based approaches in the evaluated UAV–fog traffic monitoring system.

As Uncrewed Aerial Vehicles (UAV) have been used widely in a variety of real-world monitoring applications, quality design of UAV-based monitoring systems becomes an emergent challenge as it involves complex trade-offs among several performance criteria. While analytical models have been used for performance analysis of UAV systems, they often rely on hypothetical parameter values due to difficulty in accessing real-world systems, resulting in a gap between theory and practice. To fill this gap, this paper proposes SPADE (Simulator-assisted PerformAbility Design methodology for UAV-based Systems), an approach that integrates performance profiling with a realistic flight scenario generated by a UAV simulator and model-based performance analysis. We demonstrate the application of SPADE through a case study that focuses on designing a UAV-based ecological monitoring system using an object detection algorithm (YOLOv5). Our analysis explores key trade-offs among several quality metrics, including detection accuracy, performance, energy consumption, and service availability. Using Stochastic Petri Nets, we conduct numerical evaluations, with baseline parameter values estimated from performance profiling on an emulated computing device. Experimental results using YOLOv5 provide valuable insights into how image resolution and computation modes impact UAV-based system performance and availability. These findings offer practical guidance for improving UAV system design.

Large Language Models (LLMs) have been increasingly adopted in a wide range of applications, many of which require long-running inference processes. However, these systems may be subject to software aging phenomena, leading to progressive performance degradation and potential failures. In this work, we experimentally investigate memory-related software aging in LLM inference. We performed 48-hour experiments with three open-source models (Pythia, OPT, and GPT-Neo) under low, medium, and high workloads, monitoring memory consumption at both system and process levels. Using the Mann–Kendall test and Sen’s slope estimator, we observed monotonic growth in RAM usage across all models on Central Processing Units (CPUs), with OPT presenting the steepest slopes. Process-level analysis further revealed that LLM processes were the primary contributors to memory growth, along with background services. Additionally, we conducted identical experiments on Graphics Processing Units (GPUs). Unlike the experiments without a GPU, GPU-based experiments revealed bounded oscillations and abrupt resets likely due to driver-level memory management, while host RAM and process-level monitoring still revealed clear symptoms of aging. These findings demonstrate that software aging manifests differently across execution environments, reinforcing the need for environment-specific monitoring approaches.

Unmanned Aerial Vehicles (UAVs) have emerged as a transformative technology for real-time road traffic monitoring, offering enhanced efficiency and responsiveness to modern traffic management systems. However, the resource limitations of UAVs and the dynamic nature of traffic densities present significant challenges for continuous operation. To address these constraints, this study proposes a vehicle-density-aware adaptive offloading mechanism that dynamically alternates between local processing and task offloading to fog nodes, based on real-time traffic conditions. The mechanism operates in three distinct modes: Low-CPU Mode for low vehicle density, Full Offloading Mode for moderate density, and Local Processing Mode for high-density scenarios. Preliminary results reveal that the proposed VD-aware adaptive offloading mechanism effectively balances performance, resource efficiency, and communication costs. It maintains competitive accuracy, optimizes throughput, and dynamically manages CPU utilization and communication overhead. These findings highlight the adaptability and efficiency of the proposed mechanism, making it an ideal solution for UAV-based road traffic monitoring in dynamic and resource-constrained environments.

This paper introduces EdgeWidgets, a versatile IoT platform designed for environmental monitoring applications. The platform integrates a Bosch BNO055 inclinometer with an ESP32 C3 SuperMini microcontroller and implements a dual-protocol communication layer (HTTP and MQTT). Our experimental evaluation shows that MQTT achieves approximately 26.1% lower latency compared to HTTP, with significantly more consistent performance. These results demonstrate the substantial performance benefits of MQTT’s persistent connection design for monitoring in connectivity-challenged environments.

ABSTRACT Objective: NoSQL database management systems (DBMSs) are designed to handle large‐scale data for modern applications. These systems often operate in a distributed manner, allowing data to be spread across multiple nodes to ensure replication, reduce data loss, and facilitate recovery. The consistency level in these DBMSs determines how synchronized data is across nodes, influencing the trade‐off among consistency, availability, and system performance. Given that different applications have unique requirements, understanding the impact of various consistency levels is essential. This study conducts an in‐depth analysis of how consistency level choices affect the performance of three leading NoSQL DBMSs: Cassandra, MongoDB, and Redis. Methods: These systems were evaluated under different consistency configurations, user loads, and workloads, with performance metrics including average response time and operations per second. Results: Our results show that Cassandra and Redis handle write operations faster than MongoDB, though Cassandra experiences significant slowdowns of up to 200% when switching to strong consistency. This performance degradation is observed for both read and write operations, making Cassandra the most affected DBMS when opting for strong consistency. Conclusion: The detailed findings offer valuable insights into the trade‐offs between performance and consistency in these DBMSs, providing guidance for database engineers in selecting appropriate consistency levels based on their application needs.

With the widespread application of Uncrewed Aerial Vehicles (UAVs) in various real-world surveillance scenarios, the quality analysis of UAV-based monitoring systems has become an emergent challenge. Previous model-based studies often made theoretical assumptions to estimate the performance and availability of UAV computing systems, without detailed considerations of the interaction between UAVs and fog computing nodes, as well as computation steps of object detection algorithms. In this paper, we propose Stochastic Reward Nets (SRNs) to capture computational behavior and analyze performance, availability, and performability metrics of a UAV system that utilizes computation offloading. In order to obtain more realistic parameters for model-based analysis, we conduct empirical experiments using an edge computing device to emulate real-time object detection on a UAV. We measure the throughput of real-time object detection process in three stages by experiments that are fed into parameters for numerical analysis on the proposed model. Through the sensitivity analysis, we demonstrate the impact of different computation modes and video resolutions on performance and availability metrics, providing insights for improving UAV system design and operation.

Uncrewed Aerial Vehicles (UAVs) have been used in mission-critical scenarios such as Search and Rescue (SAR) missions. In such a mission-critical scenario, flight autonomy is a key performance metric that quantifies how long the UAV can continue the flight with a given battery charge. In a UAV running multiple software applications, flight autonomy can also be impacted by faulty application processes that excessively consume energy. In this article, we propose Flight Autonomy Assurance as a framework to assure the autonomy of a UAV considering faulty application processes through performability modeling and analysis. The framework employs hierarchically configured stochastic Petri nets, evaluates the performability-related metrics, and guides the design of mitigation strategies to improve autonomy. We consider a SAR mission as a case study and evaluate the feasibility of the framework through extensive numerical experiments. The numerical results quantitatively show how autonomy is enhanced by offloading and restarting faulty application processes.

Image classifiers for recognizing real-world objects are widely used in the Internet of Things (IoT) and Cyber-Physical Systems(CPSs). A classifier is trained offline by machine learning algorithms with training data sets, and then it is deployed on a cloud or an edge computing system for online label predictions. As the classifier's performance depends on the underlying software infrastructure, it may degrade over time due to software faults causing software aging. In this paper, we address this issue and experimentally investigate software aging observed in an image classification system that continuously runs on cloud and edge computing environments. We apply several statistical techniques to analyze degradation trends in the systems under stress tests. Our statistical trend analysis confirms the degradation trends in the throughput as well as the available memory resources both in the cloud and the edge environments. Contrary to our expectation, the edge computing environment under test had much less impact on the performance degradation than our cloud environment when the workload is high, although the latter one has four times larger allocated memory resources. We also show that the observed performance degradation trends are associated with the memory usage of specific processes by performing correlation analysis.

Efficient and effective object detection is a key problem in Computer Vision. Numerous object detection algorithms have been developed, whose aim is to achieve two conflicting goals, namely accuracy and efficiency, while being executed in real-time with high robustness. Many of these algorithms must run for an extended period of time, i.e., in video surveillance or in self-driving cars – a working condition that make them subject to the risk of software aging.In this work, we focus on evaluating several object detection algorithms to understand if and to what extent they are affected by software aging. A measurement-based aging approach was adopted, with a series of long-running tests and subsequent data analysis. The results report significant trends of performance degradation, sometimes leading to aging-related failures, as well as memory consumption trends, which turned out to be the main issue across all the experiments.

Home automation or domotics is a typical representative of everything as a service (XaaS). Individual houses are equipped with Internet of Things (IoT) sensors and home facilities capable of self-assessment to offer comfortable, secure, and high-quality home services to their residents. However, assessing such systems with a high level of diversity is paramount of importance and challenging to assimilate. Domotics XaaS requires a high quality of service (QoS) in service performance and operational availability. In that regard, we propose, in this paper, a modeling approach based on stochastic Petri nets (SPN) for the performability quantification of domotics architectures. SPN performability models are developed following the architecture of a home automation system consisting of several IoT sensors/devices to evaluate the trade-offs between performance and availability of home automation services. The inter-dependency between performance and availability metrics is evaluated. The metrics include, for example, the mean response time (MRT) and the number of discarded packets. Sensitivity analysis using the design of experiments (DoE) is performed to identify the system's impacting components and performability bottleneck. Simulation results highlight the useful aspects of the proposed performability models for architectural and operational optimization of home automation XaaS infrastructures.

Smart drone systems have built-in computing resources for processing real-world images captured by cameras to recognize their surroundings. Due to limited resources and battery constraints, resource-intensive image processing tasks cannot always run on drones. Thus, offloading computation tasks to any available node in a fog computing infrastructure can be considered as a promising solution. An important challenge when applying fog offloading is deciding when to start or stop offloading, taking into account performance and availability impacts under varying workloads and communication link states. In this paper, we present a performability-aware adaptive offloading scheme called PA-Offload that controls the offloading of image processing tasks from a drone to a fog node. To incorporate uncertainty factors, we introduce Stochastic Reward Nets (SRNs) to model the entire system behavior and compute a performability metric that is a composite measure of service throughput and system availability. The estimated performability value is then used to determine when to start or stop the offloading in order to make a better trade-off between performance and availability. Our numerical experiments show the effectiveness of PA-offload in terms of performability compared to non-adaptive fog offloading schemes.

Availability of a computing process running on a flying drone is an essential quality aspect for mission-critical drone systems. Computing tasks such as image processing tasks can be lost when the process encounters a failure. Since the failure probability of the process depends on workload intensities, reducing drone workloads by computation offloading or load-balancing must have impacts on the system availability. While many existing studies discuss the performance-cost tradeoff associated with computation offloading, potential impacts on the system availability have not been deeply investigated yet. In this paper, we propose stochastic models for estimating the availability, the performance, and the energy consumption of a drone system with image processing tasks that can be either offloaded to a fog node or distributed to a collaborative drone. Our comprehensive numerical analysis with the proposed model clarifies the trade-offs among the availability, the throughputs, and the energy consumption under different computation modes. Furthermore, we propose an adaptive offloading scheme that can change the computation modes dynamically according to workload intensities and network conditions. A simulation study with a phased mission scenario shows that the proposed adaptive scheme can achieve high availability with 26% of energy reduction and less than 4% of throughput losses.

Modern networks are dynamic with configuration changes that introduces a set of challenge to the network administrator in terms of security and availability. Here, the major challenge faced by the administrator is the increasing number of vulnerabilities with the uncertainties related to defense deployment options and how these options affect the network availability over time. This work proposes a hierarchical model-based approach to evaluate the availability of dynamic networks considering the deployment of different hardening options. In particular, this work adopts reliability block diagrams and Petri nets to represent and analyze dynamic network environments and evaluate their availability. A case study is presented to demonstrate the feasibility, usefulness, and scalability of the proposed approach for computing the availability of dynamic networks considering different hardening options. The proposed approach can be helpful for network administrators who are in charge of choosing the best hardening options taking into account the impacts on availability.

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.

Systems unavailability may produce severe consequences for modern business such as data loss, customer dissatisfaction, and subsequent revenue loss. Disaster recovery (DR) solutions have been adopted by many organizations as an attempt to prevent data loss and ensure business continuity. With the cloud computing expansion, different cloud providers have been offering low-cost solutions for DR purposes such as the Backup-as-a-service (BaaS) for consumers. Therefore, in this paper, we present an integrated model-experiment approach to evaluate a BaaS environment for DR purposes. We use analytic models and fault-injection experiments to evaluate DR keymetrics such as availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO) in a real-world BaaS environment. The results revealed that the environment availability can vary according to the amount of data to backed up and restored. Besides, a sensitivity analysis shows that the RTO and RPO are mainly influenced by the the mean time to recover from a disaster and the backup interval, respectively.

Data center has evolved dramatically in the past few years, and this change has to do with the advent of cloud computing and the massive increase of data due to the Internet of Everything. This new paradigm demands high availability with low costs, which represents conflicting requirements. Approaches to conduct an integrated evaluation of availability, cost and sustainability of IT (Information Technology) data center systems are not trivial. This paper proposes a set of models for estimating these metrics. A case study is conducted to illustrate the applicability of the proposed models for analyzing IT data center systems. The results revealed that we were able to upraise the availability over 70% with a small increase on the cost (around 15%) and on the sustainability (5%).

Relational databases are the most popular database system worldwide. The occurrence of failures in these systems may produce severe consequences for the business, such as data loss, customer dissatisfaction, and subsequent revenue loss. Consequently, many organizations have adopted disaster recovery (DR) solutions as an attempt to prevent data loss and ensure business continuity. Data replication for databases is one of the most used DR solution employed to guarantee data safety and availability. However, the analysis regarding DR aspects has been less explored. Therefore, in this paper, we present an integrated model-experiment approach to evaluate replication mechanisms in relational databases for DR purposes. We performed experiments in a geo-distributed cloud environment and developed analytic models to evaluate DR key-metrics such as availability, downtime, Recovery Time Objective (RTO), and Recovery Point Objective (RPO). The results revealed that the adoption of replication mechanisms could increase the system's availability significantly. It also revealed that the replication mechanisms can guarantee RPO and RTO within seconds.

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.

The Information Technology (IT) systems of most organizations must support their operations 24 hours a day, 7 days a week. Systems unavailability may have serious consequences such as data loss, customer dissatisfaction, and subsequent revenue loss. With the popularity of cloud computing, the adoption of cloud-based disaster recovery (DR) solutions has gained more space to prevent data loss and ensure business continuity. However, disaster recovery solutions are not cheap and do not exist as a single solution that suits every requirement (e.g., availability and costs). In this paper, we present an integrated model-experiment approach to evaluate cloud-based disaster recovery solutions. We use Stochastic Petri Nets (SPNs) and fault-injection experiments to evaluate availability related metrics like steady-state availability and downtime. To demonstrate the feasibility of our approach, distinct real-world cloud-based DR solutions (e.g., active/active and active/standby) are modeled and analyzed. The results revealed that disaster recovery solution significantly improves system availability and minimizes the downtime costs. In addition, our numerical analysis shows the statistical correspondence between the results of the experiments and models.

The demand for mobile devices and applications through the marketplaces has increased dramatically in recent years. Mobile applications take on even greater prominence and importance in the IT industry. Along with the growth of mobile technology, battery lifetime has become one of the most relevant challenges for this kind of device in the last few years. This paper presents a study assessing performance and energy consumption for mobile applications by means of stochastic models. In order to assist inexperienced system designers to create formal models, mapping rules were adopted to transform SysML diagrams into Deterministic and Stochastic Petri nets. The results of this work can help system designers in the design-making process in order to develop more efficient mobile applications. The estimates obtained from the models show that the proposed approach is indeed a good approximation to the respective measures obtained from the real infrastructure.

Cloud computing is a new paradigm that provides services through the Internet. Such paradigm has the influence of the previous available technologies (e.g., cluster, peer-to-peer and grid computing) and has been adopted to reduce costs, to provide flexibility and to make management easier. Companies like Google, Amazon, Microsoft, IBM, HP, Yahoo, Oracle, and EMC have conducted significant investments on cloud infrastructure to provide services with high availability levels. The advantages of cloud computing allowed the construction of digital libraries that represent collections of information. This system demands high reliability and studies regarding analysis of availability are important due to the relevance of conservation and dissemination of the scientific and literature information. This paper proposes an approach to model and evaluate the availability of a digital library. A case study is conducted to show the applicability of the proposed approach. The obtained results are useful for the design of this system since missing data can lead to various errors and incalculable losses.

Disasters can strike any time, anywhere, and most usually occur with little or no warning. Many small and mid-size businesses (SMBs) are not adequately prepared to handle a major outage. A potential solution is the the use of cloud computing to enable a disaster recovery solution for SMBs, alleviating the burden of allocating resources which would be used only in a catastrophic situation. This paper describes an experiment-based approach for studying the dependability of a disaster recovery solution supported by a private cloud. We employ fault injection to verify the benefits and drawbacks of such a solution during the design and early deployment phases. Dependability measures obtained in the testbed can be used to adjust specific components of the solution, to cross-check analytical and simulation models, as well as to give foundations for the definition of service level agreements with customers.

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.

Data backup operation is an essential part of common IT system administration to protect against data loss caused by any storage failures, human errors, or disasters. Lost data can be recovered from the backed up data if it exists. Since the backup and restore operations accrue downtime overhead or performance degradation, they have to be designed to ensure the data reliability while minimizing the performance and availability overhead. In this paper, we study the impacts of different backup policies on availability measures such as storage availability, system availability, and user-perceived availability. Backup and restore operations are designed using SysML Activity diagrams that are automatically translated into Stochastic Reward Net (SRN) to compute the availability measures. Our numerical results show the effectiveness of the combination of full backup and partial backup in terms of user-perceived data availability and data loss rate. Furthermore, the sensitivity ranking can help improve the availability measures.

High-availability assurance of server systems is becoming an important issue, since many mission-critical applications are implemented on server systems. To achieve high-availability, software rejuvenation is a practical technique to reduce unexpected downtime caused by software aging in software applications running on server systems. Although analytic models of software rejuvenation are well-studied, such analysis is not used in server system administration due to the complexity of modeling. In this paper, we present an availability modeling method for server system with software rejuvenation based on SysML that is used to describe system configurations and maintenance operations semi-formally. The proposed approach allows system administrators, who do not have expertise in availability modeling, to design and study the effects of different rejuvenation policies deployed in server systems. To show the applicability of the proposed modeling and evaluation process, a case study of a web application server is presented. We show the correctness of our modeling method by comparing the conventional models for condition-based and time-based software rejuvenation.

High-availability assurance of cloud service is a critical and challenging issue for cloud service providers. To quantify the availability of cloud services from both architectural and operational points of views, availability modeling and evaluation are essential. This paper presents a component-based availability modeling framework, named Candy, which constructs a comprehensive availability model semi-automatically from system specifications described by Systems Modeling Language (SysML). SysML diagrams are translated into components of availability model and the components are assembled together to form the entire availability model in Stochastic Reward Nets (SRNs). In order to incorporate the maintenance operations of cloud services in availability models, Candy defines the translation rules from Activity diagram to SRN and synchronizes the related SRNs according to SysML allocation notations. The feasibility of the proposed modeling and availability evaluation process is studied by an illustrative example of a web application service hosted on a cloud infrastructure having multiple failure isolation zones and automatic scale-up function.

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.