Ana Fernandez-Vilas

Diego Cajaraville-Aboy, Manuel Fernández-Veiga, Ana Fernández-Vilas et al.

Fluid Computing aims to support distributed applications execution across heterogeneous cloud, edge, and device resources, motivating task execution mechanisms that adapt to dynamic and privacy-sensitive environments under runtime conditions. In this context, current task offloading schemes rarely address privacy risks and information leakage under adversarial execution settings; furthermore, most coded computing proposals focus on straggler mitigation without considering system-level objectives such as energy awareness. This paper proposes a coded task offloading scheme for D2D networks under stochastic task arrivals and queue-based dynamics. The proposal combines task offloading techniques with linear secret sharing schemes, where tasks are encoded into redundant shares to support threshold-based recovery, straggler mitigation, and privacy preservation while enhancing system performance. Then, we formulate a privacy-aware offloading problem that jointly optimizes delay and energy while penalizing the theoretical privacy leakage of coded tasks under noisy leakage observations. The problem is solved using a branch-and-bound solver alongside a lightweight heuristic scheduler, both of which are evaluated through a discrete-event simulator. Results show that coded offloading improves the delay--energy trade-off with respect to classical full and parallel offloading schemes, while the heuristic achieves near-optimal performance, outperforming baseline and state-of-the-art solvers. The results also show how privacy leakage penalties reshape offloading decisions, exposing an inherent delay--energy--privacy trade-off.

David Barral, Aitor Brazaola-Vicario, Diego Cifrián et al.

QKD technology is being increasingly adopted inside the network core for protecting information transport against any form of computational attacks. However, the use of QKD for wide-area internetworking is still challenging and costly, due to its strong trust assumptions and the low achievable key rates in long QKD links. This paper presents a standards-driven design and implementation of a unified hybrid key delivery service for a network of isolated QKD domains (subnetworks using QKD as provider technology for secret key generation) connected via classical WAN links. The framework follows a distributed architecture and uses a hybrid approach where keys generated in a domain are securely relayed to other domains with PQC (Kyber), dynamically routed, and managed at the system level. The solution has been implemented in an operational testbed comprising three regional subnetworks. We present the design principles, the deployment, and the experimental performance results for this scalable key delivery service.

Javier Vecino Peñas, Ana Fernández-Vilas, Rebeca P. Díaz-Redondo et al.

Quantum networks rely on the efficient distribution of entanglement to enable long-distance quantum communication and information processing. A key challenge in these networks is the design of routing protocols capable of maintaining high quality entanglement in the presence of noise, decoherence, and imperfect operations, which progressively degrade the fidelity of entangled states through entanglement swapping. Entanglement purification provides an effective mechanism to mitigate this degradation at the cost of additional resources. In this work, we study purification-aware quantum routing and formulate the problem of selecting optimal purification strategies as an optimization task. By employing dynamic programming techniques, we identify strategies that optimally balance resource consumption and end-to-end fidelity, demonstrating the effectiveness of our approach across different scenarios.

Diego Cajaraville-Aboy, Ana Fernández-Vilas, Rebeca P. Díaz-Redondo et al.

Distributed AI and IoT applications increasingly execute across heterogeneous resources spanning end devices, edge/fog infrastructure, and cloud platforms, often under different administrative domains. Fluid Computing has emerged as a promising paradigm for enhancing massive resource management across the computing continuum by treating such resources as a unified fabric, enabling optimal service-agnostic deployments driven by application requirements. However, existing solutions remain largely centralized and often do not explicitly address multi-domain considerations. This paper proposes an agnostic multi-domain orchestration architecture for fluid computing environments. The orchestration plane enables decentralized coordination among domains that maintain local autonomy while jointly realizing intent-based deployment requests from tenants, ensuring end-to-end placement and execution. To this end, the architecture elevates domain-side control services as first-class capabilities to support application-level enhancement at runtime. As a representative use case, we consider a multi-domain Decentralized Federated Learning (DFL) deployment under Byzantine threats. We leverage domain-side capabilities to enhance Byzantine security by introducing FU-HST, an SDN-enabled multi-domain anomaly detection mechanism that complements Byzantine-robust aggregation. We validate the approach via simulation in single- and multi-domain settings, evaluating anomaly detection, DFL performance, and computation/communication overhead.