Abstract
Cities worldwide have agreed on ambitious goals regarding carbon neutrality. To do so, policymakers seek ways to foster smarter and cleaner transportation solutions. However, citizens lack awareness of their carbon footprint and of greener mobility alternatives such as public transports. With this, three main challenges emerge: (i) increase users’ awareness regarding their carbon footprint, (ii) provide personalized recommendations and incentives for using sustainable transportation alternatives and, (iii) guarantee that any personal data collected from the user is kept private. This paper addresses these challenges by proposing a new methodology. Created under the FranchetAI project, the methodology combines federated Artificial Intelligence (AI) and Greenhouse Gas (GHG) estimation models to calculate the carbon footprint of users when choosing different transportation modes (e.g., foot, car, bus). Through a mobile application that keeps the privacy of users’ personal information, the project aims at providing detailed reports to inform citizens about their impact on the environment, and an incentive program to promote the usage of more sustainable mobility alternatives.

Abstract
As different eBPF libraries keep emerging, developers are left with the hard task of choosing the right one. Until now, this choice has been based on functional requirements (e.g., programming language support, development workflow), while quantitative metrics have been left out of the equation. In this paper, we argue that efficiency metrics such as performance, resource usage, and data collection fidelity also need to be considered for making an informed decision. We show it through an experimental study comparing five popular libraries: bpftrace, BCC, libbpf, ebpf-go, and Aya. For each, we implement three representative eBPF-based tools and evaluate them under different storage I/O workloads. Our results show that each library has its own strengths and weaknesses, as their specific features lead to distinct trade-offs across the selected efficiency metrics. These results further motivate experimental studies to increase the community's understanding of the eBPF ecosystem. © 2025 Elsevier B.V., All rights reserved.

Abstract
We present LAZYFS, a new fault injection tool that simplifies the debugging and reproduction of complex data durability bugs experienced by databases, key-value stores, and other data-centric systems in crashes. Our tool simulates persistence properties of POSIX file systems (e.g., operations ordering and atomicity) and enables users to inject lost and torn write faults with a precise and controlled approach. Further, it provides profiling information about the system's operations flow and persisted data, enabling users to better understand the root cause of errors. We use LAZYFS to study seven important systems: PostgreSQL, etcd, Zookeeper, Redis, LevelDB, PebblesDB, and Lightning Network. Our fault injection campaign shows that LAZYFS automates and facilitates the reproduction of five known bug reports containing manual and complex reproducibility steps. Further, it aids in understanding and reproducing seven ambiguous bugs reported by users. Finally, LAZYFS is used to find eight new bugs, which lead to data loss, corruption, and unavailability.

Abstract
We present DIO, a generic tool for observing inefficient and erroneous I/O interactions between applications and in-kernel storage systems that lead to performance, dependability, and correctness issues. DIO facilitates the analysis and enables near real-time visualization of complex I/O patterns for data-intensive applications generating millions of storage requests. This is achieved by non-intrusively intercepting system calls, enriching collected data with relevant context, and providing timely analysis and visualization for traced events. We demonstrate its usefulness by analyzing two production-level applications. Results show that DIO enables diagnosing resource contention in multi-threaded I/O that leads to high tail latency and erroneous file accesses that cause data loss.

Abstract
We present DIO, a generic tool for observing inefficient and erroneous I/O interactions between applications and in-kernel storage backends that lead to performance, dependability, and correctness issues. DIO eases the analysis and enables near real-time visualization of complex I/O patterns for data-intensive applications generating millions of storage requests. This is achieved by non-intrusively intercepting system calls, enriching collected data with relevant context, and providing timely analysis and visualization for traced events. We demonstrate its usefulness by analyzing four production-level applications. Results show that DIO enables diagnosing inefficient I/O patterns that lead to poor application performance, unexpected and redundant I/O calls caused by high-level libraries, resource contention in multithreaded I/O that leads to high tail latency, and erroneous file accesses that cause data loss. Moreover, through a detailed evaluation, we show that, when comparing DIO's inline diagnosis pipeline with a similar state-of-the-art solution, our system captures up to 28x more events while keeping tracing performance overhead between 14% and 51%.