The environmental sustainability of Information Technology (IT) has emerged as a critical concern, driven by the need to reduce both energy consumption and greenhouse gas (GHG) emissions. In the context of cloud-native applications deployed across the cloud–edge continuum, this challenge translates into identifying energy-efficient deployment strategies that consider not only the computational demands of application components but also the environmental impact of the nodes on which they are executed. Generating deployment plans that account for these dynamic factors is non-trivial, due to fluctuations in application behaviour and variations in the carbon intensity of infrastructure nodes. In this paper, we present an approach for the automatic generation of deployment plans guided by green constraints. These constraints are derived from a continuous analysis of energy consumption patterns, inter-component communication, and the environmental characteristics of the underlying infrastructure. This paper introduces a methodology and architecture for the generation of a set of green-aware constraints that inform the scheduler to produce environmentally friendly deployment plans. We demonstrate how these constraints can be automatically learned and updated over time using monitoring data, enabling adaptive, energy-aware orchestration. The proposed approach is validated through realistic deployment scenarios of a cloud-native application, showcasing its effectiveness in reducing energy usage and associated emissions.

Knowledge Graphs (KGs) are powerful tools for organizing and extracting knowledge across various domains. However, KGs scaling in size and complexity make operations such as query answering and machine learning more challenging. These challenges are especially pressing in resource-constrained environments, where sustainability and efficiency become critical design goals, and can be addressed through controlled KG reduction that preserves semantic integrity. In this context, this paper introduces a novel application of approximate computing through controlled entity ablation for efficient KG reduction. We systematically evaluate multiple ablation strategies with different KG embedding techniques, analysing their impact on link prediction accuracy, semantic integrity, and sustainability metrics. The results demonstrate that selective and controlled ablation (of up to 20% of the entities) preserves the core semantic structure of KGs while yielding reductions in energy consumption (of up to 11,5%) and computational overhead (up to 10% savings in total training time). Furthermore, we propose a machine learning-based framework to dynamically assess and optimise ablation strategies based on the KG characteristics and the desired quality thresholds. This work underscores the potential of approximate computing paradigms to provide sustainable solutions that meet the demands of modern data-intensive applications.

Cloud-Edge application deployment involves placing multiple software components on infrastructural topologies of heterogeneous nodes, ranging from Cloud servers to Internet-of-Things (IoT) edge devices. When multiple versions (or “ flavours ”) of a component are available, application managers must select a flavour for each deployed component, and assign these components to specific nodes, all while considering constraints such as dependencies, quality of service (QoS), budget, operational costs, and carbon emissions. In complex scenarios, finding the optimal deployment is often infeasible for human operators without automated tools to systematically explore the solution space. To address this challenge, we introduce FREEDA, a first constraint optimisation approach for deploying constrained and multi-flavoured applications on Cloud-Edge infrastructure topologies. We demonstrate the practical feasibility of FREEDA through experiments on a variety of realistic Cloud-Edge infrastructural topologies and component architectures. Furthermore, we benchmark FREEDA against Zephyrus, a comparable tool employing the same underlying solving technology. Empirical results show that FREEDA achieves strong scalability across a broad spectrum of realistic configurations and consistently outperforms Zephyrus.

The need to reduce the environmental impact has led IT providers to reconsider how physical resources are managed and assigned to different applications. However, application developers play a relevant role and should design their applications in a context-aware way to adapt to the availability of renewable energy. This generates a trade-off between environmental impact and user experience. In this work, we explore this trade-off in the context of search engines. To this end, we conducted an interview-based study to understand the impact of sustainability awareness and other factors on the final users’ choices.

Due to the ever-growing demand for computational resources, the environmental impact of data centers is continuously increasing. Recently, a great effort has been made to mitigate this impact, while the demand for computational resources has continued to grow. Current mitigation strategies focus on the infrastructure perspective, while the application perspective has been neglected. This paper aims to engage application designers and developers on the path to greener application design. Following the Sustainable Application Design Process (SADP) methodology, we introduce a Sustainable Application Design Architecture (SADA) for enriching cloud-native application components with sustainability features that can be exploited to adapt the application workflow to the environmental context. The architecture enables synergies from design to deployment between all stakeholders involved in the application management. The paper focuses on enriching the application with sustainability features in the design and development phases. We also present and discuss a prototype that can translate design-level sustainability features into development features.

Data as a Service (DaaS) offers an effective provisioning model able to exploit the advantages of cloud computing in terms of accessibility and scalability when data providers need to make their data available to different data consumers. Nevertheless, in settings where data are generated at the edge and they need to be propagated (e.g., Industry 4.0, Smart Cities), DaaS model suffers of some limitations: data transfer from the edge to the cloud – and viceversa – could require a significant time and privacy issues could hamper the possibility to move the data. Goal of this paper is to propose a DaaS model based on the Fog Computing paradigm, which combines the advantages of both cloud and edge computing. The proposed solution implements an adaptive multi-agent system where each agent autonomously manages the placement of data in the most convenient location considering the quality of service requirements of the user that it is serving. To guarantee the collaboration of the agents without imposing a centralized control, a reinforcement learning algorithm will be enacted to balance between the local optimum for the single data consumers and the satisfaction of the global requirements of all consumers.

Modern cloud applications are required to be distributed, scalable, and reliable. The microservice architectural style enables developers to achieve this goal with reduced effort. Nonetheless, microservices deployment is not trivial due to the heterogeneity of the microservices in terms of both functional and non-functional requirements. This is also true when considering the monitoring requirements that are specific to each microservice and must be satisfied in order to enable the verification of the application objectives satisfaction. However, not all providers offer the same set of metrics with the same quality. The goal of this paper is to provide an approach for supporting the deployment of microservices in multi-cloud environments focusing on the Quality of Monitoring. Adopting a multi-objective mixed integer linear optimisation problem, our approach supports the application owner in finding the optimal deployment for satisfying all the constraints and maximising the quality of monitored data, while minimising the costs. To this end, a knowledge base is introduced to mediate between the perspectives of the cloud provider and the application owner, while a Bayesian Network is adopted to enhance the provider’s monitoring capabilities by estimating metrics requested by the application owners that the cloud provider is not able to monitor.

Fog computing is a continuum of resources between the cloud provider and the edge of the network. Such resources can be used to host data and computation that can be moved from the cloud provider to near the consumer in order to increase the quality of the services provided. This tutorial presents a method to select the best data or computation movement to be enacted. The tutorial is divided in three sessions. The first session introduces the fog computing architecture. The second session introduces data and computation movement: their challenges and the available technologies used to enact them. The third session presents the actual method. In particular, how the method uses a goal-based modelling language to define the requirements of consumers, the impact of data and computation movements on their requirements and how it permits to choose the best movement.

The combination of data and technology is having a high impact on the way we live. The world is getting smarter thanks to the quantity of collected and analyzed data. However, it is necessary to consider that such amount of data is continuously increasing and it is necessary to deal with novel requirements related to variety, volume, velocity, and veracity issues. In this paper we focus on veracity that is related to the presence of uncertain or imprecise data: errors, missing or invalid data can compromise the usefulness of the collected values. In such a scenario, new methods and techniques able to evaluate the quality of the available data are needed. In fact, the literature provides many data quality assessment and improvement techniques, especially for structured data, but in the Big Data era new algorithms have to be designed. We aim to provide an overview of the issues and challenges related to Data Quality assessment in the Big Data scenario. We also propose a possible solution developed by considering a smart city case study and we describe the lessons learned in the design and implementation phases.

As cities are becoming green and smart, public information systems are being revamped to adopt digital technologies. There are several sources (official or not) that can provide information related to a city. The availability of multiple sources enables the design of advanced analyses for offering valuable services to both citizens and municipalities. However, such analyses would fail if the considered data were affected by errors and uncertainties: Data Quality is one of the main requirements for the successful exploitation of the available information. This paper highlights the importance of the Data Quality evaluation in the context of geographical data sources. Moreover, we describe how the Entity Matching task can provide additional information to refine the quality assessment and, consequently, obtain a better evaluation of the reliability data sources. Data gathered from the public transportation and urban areas of Curitiba, Brazil, are used to show the strengths and effectiveness of the presented approach.

The increasing utilization of cloud resources raises several issues related to their environmental impact and, more in general, sustainability. Recently, most of the contributions have focused on energy efficiency achieved through a better physical and virtual resource management. The present paper considers instead the application level, extending the focus to the reduction of CO2emissions related to the execution of applications. We aim to exploit adaptivity through the design of an Application Controller that, enacting the right adaptation strategy for a given context, allows the improvement of the trade off between QoS and CO2emission reduction. The effectiveness of the approach has been shown running an HPC application in a federated cloud infrastructure.

With the increasing popularity of the cloud computing model and rapid proliferation of cloud infrastructures there are increasing concerns about energy consumption and consequent impact of cloud computing as a contributor to global CO2emissions. To date, little is known about how to incorporate energy consumption and CO2concerns into cloud application development and deployment decision models. In this respect, this paper describes an eco-aware approach that relies on the definition, monitoring and utilization of energy and CO2metrics combined with the use of innovative application scheduling and runtime adaptation techniques to optimize energy consumption and CO2footprint of cloud applications as well as the underlying infrastructure. The eco-aware approach involves measuring or quantifying the energy consumption and CO2at different levels of cloud computing, using that information to create scheduling and adaptation techniques that contribute towards reducing the energy consumption and CO2emissions, and finally testing and validating the developed solutions in a multi-site cloud environment with the help of challenging case study applications. The experimental and validation results show the potential of the eco-aware approach to significantly reduce the CO2footprint and consequent environmental impact of cloud applications.

Concerns about energy and sustainability are growing everyday involving a wide range of fields. Even Information Systems (ISs) are being influenced by the issue of reducing pollution and energy consumption and new fields are rising dealing with this topic. One of these fields is Green Information Technology (IT), which deals with energy efficiency with a focus on IT. Researchers have faced this problem according to several points of view. The purpose of this paper is to understand the trends and the future development of Green IT by analyzing the state-of-the-art and classifying existing approaches to understand which are the components that have an impact on energy efficiency in ISs and how this impact can be reduced. At first, we explore some guidelines that can help to understand the efficiency level of an organization and of an IS. Then, we discuss measurement and estimation of energy efficiency and identify which are the components that mainly contribute to energy waste and how it is possible to improve energy efficiency, both at the hardware and at the software level.

This work provides a system modeling approach to describe a virtualized data center environment running a business process. This model allows the collection of simulation data at different workload rates that can be used as a dataset for reasoning about quality of service and energy efficiency issues related to the process. The model overcomes the issue of obtaining real data from data center administrators and collecting relevant information needed for studying the process behavior. The model is flexible and can be used to model data centers of different dimensions and characteristics. It can also be used for "what-if" analysis about the system configuration, predicting the outcome of a modification over energy efficiency and quality of service.