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This paper introduces a coded task offloading scheme designed for device-to-device (D2D) networks that addresses both performance and privacy concerns in fluid computing environments. By integrating linear secret sharing with task offloading strategies, the authors optimize for delay and energy consumption while minimizing privacy leakage in adversarial settings. The results demonstrate that their approach significantly enhances the delay-energy trade-off compared to traditional offloading methods, revealing a critical interplay between privacy and performance metrics.
Coded offloading not only boosts performance but also reveals a surprising delay-energy-privacy trade-off that challenges conventional task execution strategies.
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.
