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This data article provides raw log files captured from automated computational experiments with an application integration platform that implements the task-based execution model. The code of a well-known enterprise application integration process was modified to record these data while running the experiments. The dataset described in this article is related to the research article entitled “On the Analysis of Makespan and Performance of the Task-based Execution Model for Enterprise Application Integration Platforms: an Empirical Study” of Frantz et al. (2021). The dataset contains two types of CSV files in text format presenting: (1) data of makespan related to experiment executions and (2) data regarding the number of messages and work-units processed during each experiment. Scientific community who works on workflow and integration process execution can benefit from these data to analyse and understand the behaviour of the task-based execution model, obtain insights to improve this model, as well as perform comparisons with other types of execution models or platforms.

The use of applications is important to support the business processes of companies. However, most of these applications are not designed to function collaboratively. An integration solution orchestrates a group of applications, allowing data and functionality reuse. The performance of an integration solution depends on the optimum configuration of the number of threads in the runtime engine provided by the integration platforms. It is common that this configuration relies on the empirical knowledge of the software engineers, and it has a direct impact on the performance of integration solutions. The optimum number of threads may be found by means of simulation models. This article presents a methodology and a tool to assist with the generation of simulation models based on queuing theory, in order to find the optimum number of threads to execute an integration solution focusing on performance improvement. We introduce a case of study to demonstrate and experiments to evaluate our proposal.

The increasing demand for companies to reduce the IT infrastructure (on-premise) are driving the adoption of a type of cloud computing category known as Infrastructure-as-a-Service (IaaS) to provide virtualized computing resources over the Internet. However, the choice of an instance of virtual machine whose configuration is able to meet the demands of the company is a complex task, especially concerning the price charged by providers. The lack of transparency of the mechanism of definition of the prices adopted by providers makes difficult the decision-making process, considering the influence of several factors on the final price of the instances, among them the geographical location of the data center. In view of this problem, this work presents a new proposal of price modeling of instances using multiple linear regression model, including the geographical location of the data center as one of variables of the model. To verify the accuracy of the regression model proposed, the calculated prices were compared to real prices charged by IaaS providers Amazon EC2, Google Cloud Platform e Microsoft Azure.

The coronavirus disease that emerged in 2019 (COVID-19) is highly contagious and has given way to a global pandemic. A present COVID-19 has high transmission rates worldwide, including in small Brazilian cities such as Ijuí. Located in the northwest part of the state of Rio Grande do Sul (RS) and with a population of 83,475, Ijuí was selected as the site of a population-based survey involving 2,222 subjects, from April to June 2020. Subjects were tested for the presence of antibodies against coronavirus (SARS-CoV-2) and answered questions regarding social distance adherence (SDA), daily preventive routines (DPR), comorbidities, and sociodemographic characteristics. In parallel, the local government registered the official COVID-19 cases in Ijuí, as well as the mobile social distancing index (MSDI). In this study, we demonstrate that there was a decrease in the levels of SDA, DPR and MSDI before the beginning of COVID-19 community transmission in Ijuí. Furthermore, we provide predictions for the number of COVID-19 cases, hospitalizations, and deaths in the city. We conclude that insufficient social distancing, as evidenced by different methods, may be related to the rapid increase of COVID-19 cases in Ijuí. Our study predicts an approaching outbreak of COVID-19 in Ijuí through community spread, which could be avoided or attenuated with increased levels of social distancing among the population.