Włodzimierz Wysocki

This article demonstrates the need to optimize the use of electricity by computer devices in accordance with the concept of ESG and to analyze methods of reducing energy consumption. To achieve the goal in the article, extensive literature studies on selected topics were carried out and quantitative data were used to analyze the technical values of modern data centers. These elements are essential in today’s knowledge-based economy, where the Internet of Things and smart cities are current topics. The present article discusses the most popular techniques for reducing energy consumption in electronic systems and by software using the example of data centers, which is of fundamental importance for this type of organization and translates into the costs of the services provided. Addressing energy cost optimization affects the future of the global economy, which will be impacted by smart cities. The added value in this paper is the provided review of the methods for measuring and optimizing energy use in a knowledge-based economy, where the energy industry is the foundation of operation. Additionally, the research analysis conducted in the article is a starting point for solutions in the current economy, where smart cities and the Internet of Things are prevalent global research topics.

Managing software development processes is still a serious challenge and offers the possibility of introducing improvements that will reduce the resources needed to successfully complete projects. The article presents the original concept of classification of types of project tasks, which will allow for more beneficial use of the collected data in management support systems in the IT industry. The currently used agile management methods—described in the article—and the fact that changes during the course of projects are inevitable, were the inspiration for creating sets of tasks that occur in software development. Thanks to statistics for generating tasks and aggregating results in an iterative and incremental way, the analysis is more accurate and allows planning the further course of work in the project, selecting the optimal number of employees in task teams, and identifying bottlenecks that may decide on faster completion of the project with success. The use of data from actual software projects in the IT industry made it possible to classify the types of tasks and the necessary values for further work planning, depending on the nature of the planned software development project.