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
Modern supercomputers host numerous jobs that compete for shared storage resources, causing I/O interference and performance degradation. Solutions based on software-defined storage (SDS) emerged to address this issue by coordinating the storage environment through the enforcement of QoS policies. However, these often fail to consider the scale of modern HPC infrastructures. In this work, we explore the advantages and shortcomings of state-of-the-art SDS solutions and highlight the scale of current production clusters and their rising trends. Furthermore, we conduct the first experimental study that sheds new insights into the performance and scalability of flat and hierarchical SDS control plane designs. Our results, using the Frontera supercomputer, show that a flat design with a single controller can scale up to 2,500 nodes with an average control cycle latency of 41 ms, while hierarchical designs can handle up to 10,000 nodes with an average latency ranging between 69 and 103 ms.

Abstract
Breakthroughs in sequencing technologies led to an exponential growth of genomic data, providing novel biological insights and therapeutic applications. However, analyzing large amounts of sensitive data raises key data privacy concerns, specifically when the information is outsourced to untrusted third-party infrastructures for data storage and processing (e.g., cloud computing). We introduce Gyosa, a secure and privacy-preserving distributed genomic analysis solution. By leveraging trusted execution environments (TEEs), Gyosa allows users to confidentially delegate their GWAS analysis to untrusted infrastructures. Gyosa implements a computation partitioning scheme that reduces the computation done inside the TEEs while safeguarding the users' genomic data privacy. By integrating this security scheme in Glow, Gyosa provides a secure and distributed environment that facilitates diverse GWAS studies. The experimental evaluation validates the applicability and scalability of Gyosa, reinforcing its ability to provide enhanced security guarantees.

Abstract
We present Keigo, a concurrency-and workload-aware storage middleware that enhances the performance of log-structured merge key-value stores (LSM KVS) when they are deployed on a hierarchy of storage devices. The key observation behind Keigo is that there is no one-size-fits-all placement of data across the storage hierarchy that optimizes for all workloads. Hence, to leverage the benefits of combining different storage devices, Keigo places files across different devices based on their parallelism, I/O bandwidth, and capacity. We introduce three techniques-concurrency-aware data placement, persistent read-only caching, and context-based I/O differentiation. Keigo is portable across different LSMs, is adaptable to dynamic workloads, and does not require extensive profiling. Our system enables established production KVS such as RocksDB, LevelDB, and Speedb to benefit from heterogeneous storage setups. We evaluate Keigo using synthetic and realistic workloads, showing that it improves the throughput of production-grade LSMs up to 4x for write-and 18x for read-heavy workloads when compared to general-purpose storage systems and specialized LSM KVS.