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.

Reliable signal reception and attenuation control are critical to improving the performance of low-power wide area networks (LPWANs), especially in hybrid systems integrating LoRa and satellite communications. This study analyzes the effect of Graham’s algorithm in hybrid LoRa-satellite networks using NEO-M8N GPS-enabled TTGO nodes deployed in mesh in hindered environments. Implementation of the algorithm optimized inter-node connectivity, reduced signal attenuation, and improved satellite availability. A superior packet delivery ratio (PDR) was observed with the Graham algorithm. In addition, power consumption per minute was stabilized and battery life was extended. Through mathematical modeling and experimental validation, it is confirmed that Graham improves the energy efficiency and robustness of the network, enhancing the performance of hybrid LPWAN systems.

As urban centers increasingly grapple with the challenges of population growth, traffic congestion, and environmental degradation, the need for sustainable mobility solutions has become imperative. This study presents a performance evaluation of a public bicycle-sharing system through the application of Stochastic Petri Nets (SPNs), which are employed to model and simulate user behavior under dynamic conditions. Drawing on recent advances in urban mobility and shared transportation research, the proposed stochastic framework effectively captures key operational metrics, such as bicycles in transit, station availability, waiting probability, system utilization, and the occupancy of docking stations. A case study analyzes three high-demand stations across varying demand scenarios (baseline, 25%, and +25%). Findings reveal that, while the system performs adequately under current demand levels, it becomes vulnerable to saturation with increased usage and demonstrates operational inefficiencies when demand decreases. The model offers valuable insights to inform operational strategies, guide infrastructure development, and support public policy aimed at fostering integrated and sustainable urban mobility.

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.

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.

As organizations adopt Data Mesh to decentralize data ownership, domain teams gain autonomy to manage and evolve their data products. While this fosters architectural flexibility, it also increases the risk of table duplication across distributed environments, affecting governance and storage efficiency. This paper introduces a graph-based methodology to detect such structural redundancies by modeling data architectures as directed graphs and applying subgraph isomorphism algorithms. A user-guided validation step confirms whether identified similarities correspond to actual duplications. The method supports both synthetic and benchmark-based scenarios, including an adapted TPC-DS schema, and enables algorithmic evaluation through execution time (ET), accuracy (ACC), and success frequency (SF). As a case study, two algorithms were tested: VF2 and a hybrid approach called Node Match, which uses degree-based filtering as a pre-step before applying VF2. Node Match demonstrated superior cost-efficiency. A Python-based tool was developed to implement the methodology, enabling graph simulation, interactive validation, and automated metric reporting.

In this paper, we present a hierarchical modeling strategy that combines stochastic Petri nets (SPNs) with an iterative algorithm to evaluate performance in containerized microservices. Our approach models both synchronous and asynchronous calls, bounded queues, and network constraints, and then dynamically adjusts the number of replicas to keep the discard probability within predefined thresholds. By analyzing each microservice in a modular fashion, we avoid exploding the state space and achieve accurate predictions for different load and bandwidth scenarios. In experiments with 21 containerized microservices under three bandwidth constraints (10, 40 and 100 MB/s), our method correctly predicted throughput, container usage and bandwidth consumption within 95% confidence intervals, confirming its effectiveness for resource allocation decisions in microservice platforms.

Internet of Things (IoT) applications equip rural producers with decision support tools and automated solutions that boost agribusiness productivity, quality, and profit. However, most poultry farmers still use conventional methods of operation in which human workers carry out all routines for monitoring and controlling their farms at the expense of greater productivity. One of these human activities is manual weighing, which can be replaced by nonintrusive methods such as computational vision applications that estimate live poultry's weight using video cameras. Since Internet of Things (IoT) devices may have low computing power limiting the ability to process the data locally, they can transfer it to a fog or cloud data center, where they are processed. This article aims to conduct a dependability study of a poultry house automated with a computer vision-based system for estimating poultry weight considering hierarchical models (e.g., Markov chain, reliability block diagram, and closed-form equation) to represent the whole system and obtain steady-state availability and annual downtime. In addition, our purpose is to consider and compare different architectural solutions, such as edge and fog computing-based solutions. The proposed solution verified that a cloud-based application with no redundancy has a downtime of 34.14% and 9.176% hours when considering a hot-standby redundancy strategy in the office node of a cloud solution.

In recent years, machine learning models have used data from Hardware Performance Counters (HPCs) proposed to combat Distributed Denial of Service (DDoS). Several models use feature selection (FS) methods to deal with these datasets’ high dimensionality, seeking to develop efficient real-time detection systems. In this work, we propose a methodology to evaluate the behavior of 24 HPCs whose data is subjected to five different DDoS attacks. We adopt the FS methods Filter, Wrapper, Hybrid, and Embedded as a selection method. The results showed that three main HPCs are the most impacted and may be used to detect the attacks, keeping the results of the classification algorithms used in validation above 98%, which means an 87.5% reduction in the dimensionality of the dataset. The research results include that the Filter Approach demonstrated excellent stability in indicating the best HPCs for DDoS detection performance. In contrast, hybrid methods showed more unstable results, expanding the possibilities for future studies on the explainability of these methods. Finally, selecting essential features to detect various attack types can reduce the HPC data collection time or even discard multiplexing techniques, allowing the use of the proposed technique for real-time attack detection environments and contributing to the security of devices with fewer HPCs available, such as embedded and IoT devices.

Cloud computing is a method for accessing and managing computing resources over the internet, providing flexibility, scalability, and cost-efficiency. Cloud computing relies more and more on OS-level virtualization tools such as Docker and Podman, enabling users to create and run containers, which are widely used for application management. Given its significance in cloud infrastructures, it is crucial to have a better understanding of OS-level virtualization performance, especially in tasks related to container management (ex: creation, destruction). In this paper, we conducted benchmarking tests on Docker and Podman to evaluate their performance in various container management scenarios and with different image sizes. The results revealed that Podman excels in quickly instantiating small-sized containers, while Docker demonstrates superior performance with larger-sized containers.

Internet of Things (IoT) is a network of physical objects equipped with embedded technology, sensors, and connection to the network (e.g. vehicles, buildings, and others). These physical objects are able to collect data that will be used by various applications. Currently, there are already various applications in IoT such as smart agriculture and smart parking. These applications are composed by a set of heterogeneous components, so tasks such as resource management and maintenance of these systems become complex. Therefore, this paper presents a modeling strategy based on hierarchical models using Stochastic Petri Net (SPN) and Reliability Block Diagram (RBD). In order to evaluate the dependability in an IoT system, a case study was carried out based on a specific scenario considering a smart building. The proposed models enable to estimate measures such as steady-state availability and annual downtime.

The infrastructure necessary to support convergent network services is increasingly more complex than the usual communication networks. A network device must be able to quickly detect any communication failure between adjacent devices for the upper-layer protocol to be able rectify this failure and prevent any interruptions in services. Convergent networks use Hello routing protocol from the upper layer to detect failures. The detection period, dependent on issues intrinsic to the protocol and the arrangement of the network configuration, can lead to high downtimes in case of unavailability of the main circuit and of switch to the backup circuit. In this context, considering a real corporate network in a production environment, there is the need to carry out experiments aimed at obtaining a scenario that can offer the lowest downtime and, consequently, a greater network availability. This work is aimed at carrying out an inferential performance assessment through a paired before-and-after comparison in order to obtain the best convergence condition for a corporate network and consequently offer greater availability. For that aim, we have proposed a Continuous-Time Markov Chain (CTMC) that represents convergent network architectures considering the Border Gateway Protocol (BGP) and Bidirectional Forwarding Detection (BFD) protocol for supporting availability evaluation. In addition, we presented a closed-form equation derived from the proposed CTMC for calculating the availability of critical warm standby components by system considering its size. The work also presents a sensitivity analysis of network availability as a result of fail over time. Our approach has proven to be feasible, and it highlights the most appropriate scenarios, supporting network architects at design time.

The blockchain shared ledger emerged as an alternative to the bureaucratic banking system that may take days to confirm a payment or a transfer between clients. The blockchain concept evolved and became viable for various applications beyond the domain of financial transactions. Blockchains become a way to reach better relationships through contract validation, documents transfer, and personal and business data security. Recently, the blockchain-as-a-service has debuted on Microsoft Data Centers, and now many share an infrastructure that can change and improve their security routines. This paper evaluates the feasibility of a blockchain-as-a-service infrastructure and helps those who plan to deploy or sell blockchains. A modeling methodology based on Dynamical Reliability Block Diagrams (DRBD) is adopted to evaluate two dependability attributes: system's reliability and availability. The proposed infrastructure contains the minimum requirements to deploy the Hyperledger Cello, a platform to create and manage blockchains. The availability results pointed out a system downtime of 121 hours per year and reliability issues that must be addressed when building blockchain-as-a-service infrastructures.

The adoption of stochastic models has been one of the central topics in various architectures. One important step to adopt it is model validation, which aims at obtaining reasonable models to represent actual behavior of services components, it has been essential to validate models against actual measurements. System-wide model simulation results can be compared with recordings from the measurement. In this paper, we accomplish the model validation to Stochastic Petri Net (SPN) models created to evaluate VoD system hosted on a private cloud system, considering MP4, MPG, Ogg and FLV formats. We proposed the performance model to represent packet transfers, and to compute performance metrics, such as throughput, packet loss, and service delivery reliability. The SPN model enables a compact representation of a large number of packets generated by video streaming. We validate the models through experimental data, using a VoD streaming service in a cloud infrastructure testbed. We demonstrate that the proposed models are accurate and can be utilized for planning the quality of service (QoS) of corporate video streaming infrastructures. A case study is presented to compare the behavior of the system. Results indicate that the model validation way adopted can be a good solution for models validation. A case study is conducted to compare the behavior of the system under distinct network scenarios. The results indicate that video streaming QoS under 3G (EVDO) networks is significantly worse than other wireless technologies, such as WiFi, 3.5G (HSPA+), and 4G (LTE).

Attacks that deny access to a service provider can occur anytime, anywhere, and most usually occur with little or no warning. Many small and midsize companies are not prepared to handle a significant outage. For an enterprise to face up to an attack of this type, it must possess a bandwidth higher than that of the attack, an infrastructure with redundant components, regular backups, firewalls for monitoring the threats and other proactive and reactive mechanisms. Otherwise, the service will be interrupted, increasing the chances of financial losses. Hierarchical modeling approaches are often used to evaluate the availability of such systems, thereby leveraging the representation of multiple failure and repair events in distinct parts of the system. This paper evaluates the impact of a distributed denial-of-service attack and malicious software in computer systems. We propose hierarchical models that represent the behavior of major system components and assess the effects of a DDoS and Malware attack on the system availability. We also estimate the likelihood of an attack, attacker benefits, feasibility, the pain factor and the propensity of the offense were present. They enable a direct analytical solution for large systems. The attack tree indices show the impact of simultaneous attacks on a computer system and the several threats which will maximize the system downtime. The results obtained from the attack tree analysis allow to plan and improve system's availability, maintainability, and reliability.

The evaluation of dependability or performance of general systems usually relies on the assistance of stochastic modeling and simulation tools. Those software packages enables the creation of models and computation of metrics quickly and accurately. This paper introduces the Mercury tool, which is an integrated software that enables creating and evaluating Reliability Block Diagrams, Stochastic Petri Nets, Continuous Time Markov Chains, and Energy Flow Models. Mercury provides a graphical user interface, a script language for command-line interface, and also an API (Application Programming Interface) that enables interaction through external applications. The evaluation of models is not restricted to the assumption of Exponential distributions, which is a common constraint in other similar tools. Mercury implements a simulation framework that allows more than 25 probability distributions, as well as a moment matching method that enables expolynomial -phase-type -distributions for models solved through numerical analysis. This paper presents the main features and methods available in Mercury to aid the dependability and performance evaluation of various systems, for both academy and industry. The accuracy and applicability of the tool is illustrated by a case study of packet loss and throughput for a Video on Demand (VoD) system.

Predicting the amount of resources available to system's users has become a task of interest to services providers even with the advent of elastic cloud computing, because the number of resources is finite despite being virtually infinite on the customer view. This paper proposes a model to evaluate node's capacity in a cloud computing environment based on the amount of available hardware resources. By combining models to availability evaluation, such as reliability block diagrams, representing the operational infrastructure mode, and stochastic Petri net for capacity-oriented availability evaluation, we can determine the real amount of resources available at a predetermined time interval. Sensitivity analysis is also used to determine the component with highest impact in the metric of interest. The models, methods, and results of this research shall aid companies to plan the deployment and configuration of their services and cloud computing infrastructure.

Cloud computing has the potential to reduce the cost of systems on the Internet. Elasticity mechanisms, such as auto-scaling, enable avoiding wastes, delivering only the necessary resources. Defining and implementing an efficient auto-scaling policy is a complex task that depends on the parameter setting, types of VM contracts and the expected workload. All those variables must be taken into account when establishing the tradeoffs between performance and cost to fulfill a given servicelevel agreement (SLA). We propose a stochastic model to assist in cloud planning. The model was validated for a set of significant scenarios by comparing the respective model's results with those obtained from real system measurements. This model takes as input the auto-scaling configuration parameters and the time between user requests. The proposed model is employed to compute throughput, mean response time, and cost of the cloud computing infrastructure setup. A sensitivity analysis was also conducted for identifying the parameters impact on the system performance.

The perfect relationship between deployment costs and systems availability is one of the primary goals of companies that wish to provide some computer environment or service through the Internet. The question that everyone wants to know the answer is: How much may I save and still improve the availability of my system avoiding financial losses with an SLA contract breach? This paper attempts to respond to this question by combining some techniques used at dependability evaluation field, such like hierarchical models, sensitive analysis techniques, and analysis costs. The models shown in this paper are artifacts and scenarios for a data synchronization server hosted at a private cloud computing platform based on Eucalyptus. Each scenario presented here was generated to study the impact of redundant techniques in the system availability and deployment cost. By analyzing each architecture separately and comparing them, we can indicate the one that has the best cost-benefit relationship and attends the providers, as well as the client's needs.

Users of computer systems wish to keep their personal data safe, updated, fair and accessible by other terminals, like personal computers, smart phones, portable consoles and PDAs. To perform these activities, one technology has become popular in our daily lives: data synchronization. Companies that provide this kind of service must do it with the greatest availability possible since their clients need their data to be available whenever they want to access it, and their customers in the legal field must avoid financial losses through SLA contract breaches. This paper presents hierarchical models for evaluating the availability of a data synchronization server infrastructure. The results show an availability of 98.82% for the proposed architecture, which means an annual downtime of 103 hours, this is more than 4 days of unavailability, where users cannot perform data synchronization.

The increasing development and utilization of services based on cloud facilitates the development of sectors like entertainment through media streaming. Platforms such as Eucalyptus provide Infrastructure as a Service (IaaS), allowing to create and manage a wide variety of services on cloud. Redundant mechanisms are commonly adopted to achieve improvements of availability, since this issue is critical for providing a cloud service. This paper analyses the impact of adopting a redundant component for a VoD streaming service based on Eucalyptus platform, comparing these results to a non-redundant proposal. The proposed solution revealed an reduction of 51.01% in annual downtime compared to the non-redundant proposal. Sensitivity analysis was used to identify availability bottlenecks to the proposed redundant architecture.

Large cities face growing mobility problems, due to the major traffic jams that result from high numbers of vehicles on the roads. In response, city and national governments have invested in alternative means of urban passenger transit, such as subways, trains, as well as Bus Rapid Transit (BRT). This article aims to analyze the BRT system, by attempting to calculate the probability of reaching a destination at a specific time, thereby providing a tool that can be employed to improve the system and increase passenger confidence in it. To this end, a Continuous Time Markov Chain (CTMC) model is proposed to represent the bus stations and compute the probability metric for arrival at the destination within the specified time frame. The model allows a mathematical function to calculate the probabilities for the corresponding architecture. Two case studies were conducted in order to verify the model and illustrate its potential value in the planning of BRT systems.

The reduction of cloud computing energy consumption can be related to the application layer management with virtualization services, or a software layer. All clouds demand a power infrastructure with high availability. A data center power system is generally classified according to four redundancy levels, named tier I to IV. This paper proposes a power load distribution algorithm in depth search (PLDA-D) to optimize the power distribution of private cloud electrical infrastructures. The PLDA-D adopts the Energy Flow Model (EFM) as the basis to design power infrastructures. The EFM is a model that computes sustainability impacts and cost issues while it respects the restrictions of each component to provide energy. In addition, a case study illustrates the applicability of the proposed PLDA-D through the analysis of a private cloud running over a data center made up of tier II electrical architecture. Considerable results were achieved though the evaluation of the proposed algorithm. For instance, a reduction of 2.95% in energy consumption as well as an improvement of over 17% on the environment impact.

Cloud computing environments provide powerful storage and processing capabilities, as well as other computational resources. Due to the business potential of the pay-peruse model, as well as the advantages of easy scalability, up-to-date Video on Demand (VoD) streaming services rely on cloud infrastructures to offer a wide variety of multimedia content. This paper proposes the application of availability models to a cloud environment designed for a video streaming service. Sensitivity analysis is proposed to identify the availability bottlenecks.

Cloud computing environments are an emerging technology that aims at proving several cloud services over the Internet, making it possible to provide a huge variety of applications with different purposes to users. Multimedia services are examples of these new services that use cloud computing, like video streaming, where the user can access their videos from cloud environments. For cloud service systems, assurance of high-availability of cloud services is a challenging and critical issue. In this paper, we evaluated the video streaming service's availability for video on demand cloud system. Hierarchical modeling strategy, availability models combining reliability blocks diagrams (RBD) and continuous time Markov chain (CTMC) were used, indicating a system availability of 1.92 nines. The critical component of the system was indicated through sensibility analysis. We realized a model validation technique to demonstrate that the models represent the behavior of the real system.

Cloud computing has expanded in recent years due to many effects, such as accessibility, reliability, and collaboration. To provide those functionalities high availability is in demand, which implies a higher electric energy consumption by the computers that support the cloud infrastructure. Studies that pay attention to this electric energy consumption are important due to its impact on sustainability and operational costs. This paper proposes a power load distribution algorithm (PLDA) to optimize electrical flows of power infrastructures. The PLDA adopts the Energy Flow Model (EFM) as its basis. The EFM is a model that computes sustainability impacts and cost issues, while it respects the energy providing restrictions of each component. In addition, a case study illustrates the applicability of the proposed PLDA through the analysis of six private cloud power architectures. Considerable results were observed, including a reduction on energy consumption of 10.7%, and an improvement (reduction) on the environmental impact of over 140% was obtained.

High availability in cloud computing services is essential for maintaining customer confidence and avoiding revenue losses due to SLA violation penalties. Since the software and hardware components of cloud infrastructures may have limited reliability, fault tolerance mechanisms are a means of achieving the necessary dependability requirements. This paper investigates the benefits of a warm-standy replication mechanism in a Eucalyptus cloud computing environment. A hierarchical heterogeneous modeling approach is used to represent a redundant architecture and compare its availability to that of a non-redundant architecture. Both hardware and software failures are considered in the proposed analytical models. The results show an enhanced dependability for the proposed redundant system, as well as a decrease in the annual downtime. The results also demonstrate that the simple replacement of hardware by more reliable machines would not produce improvements in system availability to the same extent as would the fault tolerant approach.