In recent years, mobile cloud computing has attracted researchers and industries' interest since it maximizes gains with mobile computing in different areas, such as healthcare, sports, and business. However, the inherent challenges associated with mobile computing (e.g., battery provisioning and wireless network instability) may limit mobile cloud achievements. This paper provides an availability and reliability study of mobile clouds and evaluates distinct architectures and strategies. Our evaluation is based on hierarchical heterogeneous models and focuses on steady-state availability, reliability, annual downtime, and costs of implementation and provisioning of the cloud infrastructure. The results point out that service downtime may be significantly decreased by adopting actions such as spare batteries for mobile devices and the adoption of distinct redundancy strategies. We also propose a way to support decision-making considering private and public clouds by comparing some architectural choices while considering factors such as budget expenses for hardware purchases and power consumption. After 36 months, the private cloud has a cumulative cost that is smaller than the cost of a public cloud service.

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.

Cloud computing brings new technologies and concepts that favor communication services and data storage. Services like OneDrive, Google Drive and DropBox increase data availability and provide new features as synchronization and collaboration. This article aims to assess the availability and performance of private cloud storage service. A set of models that use a hierarchical strategy is proposed to evaluate both availability and performance through the composition of reliability block diagrams (RBD) and stochastic Petri net (SPN) models. A case study is presented to illustrate the applicability of the proposed models through a cloud storage service hosted on the Eucalyptus platform.

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.

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

Over the years, inbound logistics has been prominent to many industries, as globalization has forced companies to increase productivity while reducing costs. Transport modes considerably impact the costs, and a proper evaluation should consider performance, failures as well as sustainability issues. This paper presents an approach based on stochastic Petri nets (SPN) for assessing different transport modes in inbound logistics for coffee industry taking into account performability and sustainability.

Customer requirements and local legislation are forcing companies to provide green products and services. Such driving forces have resulted in the study of green supply chains (SCs), which are SCs that take into account factors that may cause positive or negative environmental impacts. However, some environmental indicators are often at odds with business goals, leading to a conflict, which, in order to optimize several goals simultaneously, is usually resolved by utilizing multicriteria decision analysis. Other modeling techniques describe systems by considering temporal aspects and causal relations. Such is the case of Petri nets, which were previously widely used to analyze performance in the context of SCs. This paper presents a method that combines the strengths of both techniques to assess the environmental and business indicators of such systems. This method adopts the exergy and the global warming potential key environmental indicators. A case study is conducted to demonstrate the application of the proposed modeling technique.

This paper proposes a methodology for modeling and evaluating supply chains based ongeneralized stochastic Petri net(GSPN) components. The proposed modeling process is based on a bottom-up approach, which assures desirable model properties that start from a set of predefined modules for typical supply chain entities. A compositional algebra that formally defines GSPN compositions is presented. The proposed methodology is supported by an implementation of the methodology in thestochastic logistics optimizer tool(SLOT). An industrial case study has been conducted to show the benefits of the approach.

The electronic funds transfer (EFT) systems promote both financial benefits for the institutions that use them and potential conveniences for their customers. EFT Systems debiting and crediting individual bank accounts from points of sale (POS) terminals in different companies and providing cash on demand 24 hours a day through automatic tellers. In the EFT system, eventual workload peaks and fluctuations can cause degradation on system performance. Hence, the performance evaluation of EFT systems has an enormous importance for electronic transactions providers, since the computing resources must be efficiently used in order to ensure high availability, reliability, scalability and security. This paper presents an stochastic model for performance evaluation and planning EFT systems processing and storage infrastructure considering load variation range. A case study is presented in order to describe the adequacy of presented approach and results.

This work proposes the use of pre-defined generalized stochastic Petri net (GSPN) components to model and evaluate outbound logistics. The modeling process takes into account the automatic generation of these stochastic models from high-level end-user-oriented descriptions. The model resultant from this translation is guaranteed to have some expected Petri nets' properties. The Stochastic Logistics Optimizer Tool (SLOT) was implemented with the aim of supporting the modeling process based on a component library. Case studies were carried out and one of them is presented.

The outbound logistics can determine the success or failure of an industry. It has a high participation in overall logistics costs and is a decisive factor for the Quality of Service (QoS). Measuring this activity and evaluating possible changes will help its management and can lead to a cost reduction and a better QoS. This paper proposes the use of predefined Generalized Stochastic Petri Nets (GSPN) components that allows the evaluation of vehicles utilization, storage levels and QoS indices. The model representing an outbound logistics scenario is obtained with a bottom-up approach, through the composition of these components, guaranteeing some expected GSPN properties. At the end of this paper, a study case made in a Brazilian meat processing industry is presented.