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

The growing adoption of drones for goods delivery has emerged as a potentially viable solution. By operating through aerial routes, drones significantly reduce delivery times and expand operational reach. However, covering large areas requires prolonged flights, leading to high battery consumption and an increased risk of collisions, particularly in densely populated regions. This study presents a Stochastic Petri Net model to evaluate drone performance, focusing on metrics such as utilization, delivery rate, mean mission time, and drop probability. Additionally, energy consumption and carbon footprint metrics were investigated to assess the environmental impact of drone operations. The model incorporates factors such as strategic recharging points and collision probability, providing insights into drone performance under high-demand scenarios.

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.

3D printing has revolutionized DIY (Do-It-Yourself) IoT prototyping, enabling cost-effective, creative custom device creation. However, this freedom also presents challenges due to the interplay between components within an IoT design, which can influence the overall utility and performance of the prototype. Optimizing these designs is difficult due to limited means of estimating their efficacy. To address this, we introduce TOAD, a novel tool for profiling IoT prototypes and gauging their performance impact. TOAD uses thermal imaging and video analysis to extract and compare design performance characteristics. Unlike existing solutions that only profile overall performance, our tool assesses component interactions and overall design effects. It offers an affordable, non-intrusive method without needing device access or code instrumentation. Extensive benchmarks show TOAD accurately extracts performance data, aiding in selecting the best design for IoT applications. Additionally, it provides insights into how casing factors like thickness and material influence thermal behavior and performance. We demonstrate practical applications by optimizing offloading decisions based on thermal behavior, highlighting casing impacts on design performance. TOAD paves the way for efficient IoT prototype designs, offering a better understanding of component interactions and significantly enhancing the utility of custom IoT designs and their effectiveness.

As the adoption of microservices architectures in cloud deployments grows, so does the challenge of ensuring rapid recovery and minimal energy consumption in the face of disasters. This paper introduces a Generalized Stochastic Petri Net (GSPN) model specifically designed to quantify recovery times and electrical consumption in such environments. The model supports the development of resilient and eco-conscious systems by allowing precise manipulation and planning based on various configuration scenarios. We identify critical architectural elements and define intervals that yield significant improvements. Our findings not only enhance the understanding of energy and recovery dynamics in microservices but also serve as a crucial tool for system designers aiming to optimize both performance and sustainability. The implications of this research facilitate a strategic approach to disaster recovery planning, contributing to the broader field of cloud computing resilience.

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.

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.

The Internet of Robotic Things (IoRT) has emerged as a game-changing player in the fourth industrial revolution (Industry 4.0) in which the robotic automation technologies are integrated with and reinforced by advanced computing paradigms like cloud/fog/IoT in order to maximize productivity of production chains of smart robotic agents in factories. A single robotic agent can comprise hundreds of sensors and actuators, and production processes performed by multiple agents can demand high computational costs, possibly only via remote resources in cloud or fog computing layers. In this context, it is paramount of importance to assimilate the performance of such computing paradigms for production chains of robotic agents in industrial factories and to provide IoRT system designers with assessment tools to comprehend and anticipate the operational performance of their projects at different development stages. This paper proposes an M/M/c/K model for the performance evaluation of IoRT systems integrated with fog-cloud computing paradigms. Focusing on routing strategies, we demonstrated that the proposed model allows evaluating how different configurations of components in an IoRT architecture impact the performance of the system using the performance metrics including mean response time, component utilization, number of messages, and drop rate. The performance modeling and evaluation of a specific IoRT in this paper can help computing system administrators to design and adopt advanced cloud/fog/IoT computing paradigms to maximize manufacturing productivity in industrial factories.

Mobile Edge Computing (MEC) is a network architecture that takes advantage of resources available at the edge of the network to enhance the mobile user experience by decreasing the service latency. MEC solutions need to dynamically allocate the requests as close as possible to their users to avoid high latency. However, the request allocation does not depend only on the geographical location of the servers, but also on their requirements. The task of choosing and allocating appropriate servers in a MEC environment is challenging because it involves many parameters. This paper proposes a Stochastic Petri Net (SPN) model to represent a MEC scenario and analyze its performance. The model focuses on parameters that can directly impact the service Mean Response Time (MRT) and resource utilization level. We propose case studies with numerical analyzes using real-world values to validate the proposed model. The main objective is to provide a practical guide to assist infrastructure administrators to adapt their architectures, finding a trade-off between MRT and resource utilization level.

Service providers may build elastic systems on public clouds. The public cloud may offer economies of scale, but there are some considerations to take into account. Infrastructure-as-a-Service (IaaS) providers charge their customers by the use of virtual machines (VMs), and wrong deployment decisions may lead to financial losses. This paper proposes an approach for estimating systems' performance, use of VM instances and its related costs on a public cloud. This work proposes a Stochastic Petri Net (SPN)-based formal modeling strategy to represent elastic systems deployed on a public cloud and a cost model to predict the use of VM instances. The approach enables designers to plan and tune elastic architectures based on Mean Response Time (MRT) estimation. Using our strategy it is possible to estimate the impact of each deployment configuration on the evaluated metrics. Our modeling strategy considers reactive scaling policies. The model represents a remote infrastructure for supporting external users requests. By combining different instance types, simultaneous jobs per VM instance, stepsizes and scaling thresholds, it is possible to offer different response times for each deployment configuration. One case study was performed to evaluate the approach. Our approach has proven to be feasible and it highlights the most effective scenarios to minimize MRT and reduce costs.

With the goal of simplifying the design and development of applications that use computation offloading, we developed the CAOS D2D platform to provide an abstraction layer for dealing with low-level tasks related to offloading of methods among Android mobile devices. This paper presents experiments performed to evaluate different aspects of the CAOS D2D, such as execution time and energy consumption of devices during method offloading. Besides that, we also performed experiments to evaluate the process of applications' dependency deployment. The experiments show that computation offloading can speedup the execution time by up to 4.55 times and reduce the power consumption by up to 88% in comparison to local executions of the same methods.

Breast cancer is currently the most common in female patients and the second with the highest mortality rate. The primary responsibility for these alarming statistical data, which has been growing in recent years, are still factors of external risks such as excessive consumption of alcohol, tobacco, processed foods, sedentary lifestyle, obesity or any item associated with an unbalanced lifestyle. Also, another major impact factor is related to late diagnosis and treatment. With this, several mechanisms, such as CAD systems, are being developed to assist specialists in rapid and early diagnosis. This work proposes an approach to reduce false positives. To evaluate and validate the proposed methodology regions extracted from the DDSM database using a CAD system were used. In the proposed methodology used texture descriptors based on functional diversity indexes for the extraction of characteristics, followed by the classification of regions of interest in mass and non-mass. The results were promising, reaching rates of accuracy, sensitivity, specificity, kappa index and area under the ROC curve of 92.29%, 90.15%, 95.65%, 0.841 and 0.939, respectively.

Mobile Cloud Computing (MCC) is a technique for increasing the performance of mobile apps and reducing their energy consumption through code and data offloading. Building an MCC infrastructure is a difficult task due to its inherent complexity and the involvement of different components. This paper proposes an approach for estimating applications' performance and data traffic volume generated by tasks offloading. This work proposes a Stochastic Petri Net (SPN)-based formal framework to represent the partitioning of applications in a method-call level. Our framework considers the available network bandwidth to send and receive tasks to the cloud. The modeling strategy represents the use and sharing of the actual available bandwidth for offloading operations. The approach enables designers to plan and tune MCC architectures based on Mean Time to Execute (MTTE) and Throughput estimation. Using our strategy it is possible to estimate the impact of the bandwidth variation on the application's MTTE and Throughput. In addition, the strategies proposed in this work may be adapted to support MCC applications in real time providing on-the-fly probabilistic performance predictions. One case study was performed to evaluate the approach. Our proposed approach has proven to be feasible and it highlights the most appropriate strategies for offloading.

Mobile Cloud Computing (MCC) helps increasing performance of intensive mobile applications by offloading heavy tasks to cloud computing infrastructures. The first step in this procedure is partitioning the application into small tasks and identifying those that are better suited for offloading. The method call partitioning strategy splits the code into a set of method calls that are offloaded to remote servers. Quite often, many applications need to make use of multiple servers for parallel processing of intensive computational operations. Predicting the behavior of such parallelizable applications is not an easy task. Deciding the number of remote servers determines the performance of the applications and the costs of the cloud usage. On one hand, users are interested in improving the performance of their applications, so they would like to use as many servers as possible, but on the other hand, they would also like to reduce their costs by using fewer cloud resources. In this paper, we propose a Stochastic Petri Net (SPN) modeling strategy to represent method call executions of mobile cloud systems. This approach enables a designer to plan and optimize MCC environments in which SPNs represent the system behavior and estimate the execution time of parallelizable applications.

Mobile Cloud Computing (MCC) combines mobile computing and cloud computing aiming to aid performance of mobile devices. The idea is simple: thin devices offload heavy methods to resource-rich servers in the clouds. We believe that in the near future MCC will adopt more advanced offloading techniques. In particular, in this paper we envision a scenario where offloading frameworks will have to deal with GPU code offloading. Amazon already offers instances with Graphics Processing Units (GPU), which can be used for this purpose. We propose and implement MCC-Adviser, a simulation tool that can predict the performance of GPUs with different number of cores using Stochastic Petri Nets. We tested MCC-Adviser in a case study with one of the expensive Amazon GPU instances. The simulations showed that it is possible to minimize costs, while satisfying user's quality of service requirements, by utilizing less powerful instances.

Elasticity is an outstanding concept of cloud computing, usually deployed through mechanisms such as auto scaling and load balancing. Cloud-based applications are able to adapt themselves dynamically to the workload behavior due to such mechanisms. The efficient instantiation of Virtual Machines (VMs) is one requirement for the elastic behavior of cloud-based applications. This study characterizes the performance of VM instantiation in a private cloud platform, considering distinct factors such as VM type, VM image size, and VM caching. We employed a full factorial design of experiments (DoE) to compute the effect and relevance of the factors as well as their interactions. Our experimental results show that the cache factor has an impact of 45.07 % on the total instantiation time, whereas the machine image (MI) has 26.45 % and the VM type only 1.05 %. The results of these experiments are also used as input parameters in a Markov chain model for sensitivity analysis. The model evaluation showed that for 6 GB and 8 GB MI, the probability of finding the MI on cache must be at least 40 % and 60 % respectively, to achieve an average instantiation time of 300 seconds. For MI with size 2 GB, such time is not exceeded even with the cache disabled. This analysis allows checking the impact of every parameter on the system response time and pointing out effective ways for improvement of performance. Such conclusions may be used as decision support for systems which often instantiate new VMs, including those using elasticity features, such as auto scaling.

Cloud accounting refers to how cloud usage is recorded and charged. We identified four federated cloud platforms which do not include an accounting system. This work presents an accounting mechanism implemented for one of these platforms focused on IaaS. Further, we analyse our proposal in a qualitative fashion.

Cloud Accounting refers to how cloud usage is recorded and charged. It is present at all commercialized cloud services (SaaS, PaaS and IaaS), since they have to monitor the consumption aiming to charge the customers. In a previous research we performed a systematic mapping study regarding cloud accounting and identified a set of shortcomings in the existing accounting models for IaaS. One of these limitations was related to charging policy specification. Charging policies are mechanisms that establish rules to convert usage records into monetary information. This paper presents a Domain Specific Language (DSL) for charging policy specification: VeloZ. Such DSL enables cloud providers to write charging policies following the economic model RaaS. According to RaaS, the IaaS must be provisioned in a high granularity in terms of resource types and such granularity is profitable for both, provider and customer. We evaluated the DSL under a real cloud platform called JiTCloud. The experiment results evidences that VeloZ can be considered feasible in terms of flexibility and reliability.

Cloud Accounting refers to how cloud usage is recorded and charged. Even in the case of a single cloud provider this task is hard and there is a context which it becomes even worse, known as federated cloud. It happens when a cloud provider dynamically outsources resources to other providers in response to demand variation. In this context, we identified some research addressing this area which developed platforms for federated clouds, however without addressing the accounting requirement. This work presents the Monext, an accounting framework for Infrastructure as a Service, focused on federated clouds. It is based on a standard accounting process, follows a set of principles specified by previous research, and offers a Domain Specific Language (DSL) to define charging policies. We evaluated the framework under a federated cloud platform called JiTCloud. The results provided evidences that Monext can be considered feasible under many aspects, such as flexibility, reliability and efficiency.