Sidechains offer partial solutions to Ethereum’s scalability challenges; however, they introduce trade-offs related to security and implementation complexity. These limitations have been further addressed by Layer-2 solutions known as rollups, which combine off-chain computation with on-chain verification, preserving both security and decentralization on the Ethereum platform. This paper proposes a Stochastic Petri Net model to evaluate the feasibility of ZK-Rollups by analyzing their impact on throughput and latency. The results indicate that increased adoption of Layer-2 transactions can enhance system throughput by up to 20%. Conversely, latency may rise by more than 100% when larger batches are used, revealing a fundamental performance trade-off.

Blockchain is a paradigm derived from distributed systems, protocols, and security concepts. However, can blockchain applications provide services in industrial environments, especially concerning performance issues? In blockchains, long response times can impair both user and service experience, and intensive resource use may increase the costs of service provision. The proposed paper tries to answer this question by evaluating the performance of one of the most popular permissioned blockchain platforms, the Hyperledger Fabric (HLF). We provide a framework for performance evaluation based on modeling and experimentation. The results indicate that block size and arrival rate can compromise throughput (by -70%), latency (by +1,500%), and environment utilization (by +28%) and that multiple gateways can reduce latency (by -75%), and throughput (by -60%).

Hyperledger Fabric stands as a leading framework for permissioned block-chain systems, ensuring data security and audit-ability for enterprise applications. As applications on this platform grow, understanding its complex configuration concerning various block-chain parameters becomes vital. These configurations significantly affect the system’s performance and cost. In this research, we introduce a Stochastic Petri Net (SPN) model to analyze Hyper-ledger Fabric’s performance, considering variations in block-chain parameters, computational resources, and transaction rates. We provide case studies to validate the utility of our model, aiding block-chain administrators in determining optimal configurations for their applications. A key observation from our model highlights the block size’s role in system response time. We noted an increased mean response time, between 1 to 25 seconds, due to variations in transaction arrival rates.

Prediction of key variables is an important part of the monitoring, control, and optimization of industrial processes, since it is important to anticipate certain behaviors so that the correct actions can be taken. To assess which algorithm is best suited to the prediction of a number of key variables at various stages of wastewater treatment plants (WWTP), five computational algorithms were researched: Artificial Neural Network, Long Short-Term Memory, deep learning Transformer model, Adaptive Neuro-Fuzzy Inference System, and Gaussian Mixture Model. With these models, techniques already well established in the state-of-the-art are evaluated, as well as more recent methods that have been exhibiting good performance in variable prediction regression problems. These algorithms were evaluated in four WWTP case studies, in which the objective is to predict the following key variables: total suspended solids, nitrate and nitrite, ammonia and ammonium, and biochemical oxygen demand. The learning process of each algorithm was performed using extensive tests in order to select the input variables, and define the topologies and hyper-parameters of the presented models by cross-validation. The results indicate that it is possible to adequately predict the four variables, and the best results were achieved by the Transformer algorithm, which presents the lower error values in the considered metrics.

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.

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.