Coded Task Offloading for Fluid Computing: A Privacy-Aware Approach under D2D Networks

Jul 16, 2026

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Fluid Computing enables distributed applications to execute across heterogeneous cloud, edge, and device resources, requiring task execution mechanisms that adapt to dynamic, privacy-sensitive conditions at runtime. Existing offloading and coded computing largely ignore privacy risks and information leakage in adversarial settings, and many emphasize straggler mitigation without energy-aware system objectives. This work proposes coded task offloading for D2D with stochastic arrivals and queue dynamics, combining redundant linear secret shares for threshold recovery, straggler mitigation, and privacy preservation. It formulates and solves a delay–energy optimization with privacy leakage penalties, using branch-and-bound and a lightweight heuristic evaluated in a discrete-event simulator.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages25

Reading Time1 h 3 min

Introduction

Fluid Computing paradigm

Task offloading and limitations

Privacy-aware offloading challenges

arXiv :2607 .08440v 1 [ cs .DC] 9 Jul 2026 CODED TASK OFFLOADING FOR FLUID COMPUTING: A PRIVACY-AWARE APPROACH UNDER D2D NETWORKS Diego Cajaraville-Aboy, Manuel Fernández-Veiga, Ana Fernández-Vilas, Rebeca P. Díaz-Redondo atlanTTic – ICLAB, Universidade de Vigo Escuela de Ingeniería de Telecomunicación, Vigo, 36310, Spain {dcajaraville,mveiga,avilas,[rebeca}@det.uvigo.es](rebeca}@det.uvigo.es) ABSTRACT Fluid Computing aims to support distributed applications execution across heterogeneous cloud, edge, and device resources, motivating task execution mechanisms that adapt to dynamic and privacysensitive 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. Keywords Coded Task Offloading · Privacy Leakage · Linear Secret Sharing · D2D Networks · Fluid Computing 1 Introduction Current digital systems are increasingly surrounded by distributed and data-driven applications, such as IoT or AI services, running across heterogeneous resources ranging from cloud-native infrastructures and edge servers to resource-constrained devices. These deployments aim to improve response delay, leverage nearby computational capabilities, and adapt execution to dynamic application requirements. Enhancing the deployment and performance of distributed applications is therefore a relevant research challenge, especially in future 6G and D2D scenarios, where communication, computation, and sensing resources are expected to be jointly managed under massive and dynamic environments [1, 2] . Moreover, distributed applications often involve the exchange or processing of sensitive information, which makes privacy and data-management requirements essential, especially in applications such as distributed AI, federated learning, etc. Several works in the state of the art have addressed this problem within the Cloud-to-Edge Continuum [3, 4], where computation, storage, and networking resources are distributed across multiple computational tiers to exploit the benefits of each tier and improve latency, energy consumption, or resource utilization. However, the literature has also highlighted that these systems are still limited by fragmented infrastructures, weak interoperability, centralized control assumptions, and poor adaptation to highly dynamic workloads [5, 6] . These limitations may lead to suboptimal placements, inefficient communications between tiers and energy consumption, thereby increasing the digital carbon footprint [7] . In addition, privacy requirements are not always integrated as a first-class design dimension, despite being essential when computation is moved across

FAQs

What problem does the paper address in existing task offloading schemes?

It targets the lack of privacy risk handling and information leakage consideration under adversarial execution, while many coded computing works focus on straggler mitigation without jointly optimizing system goals like energy awareness.

How does the proposed coded task offloading scheme preserve privacy and mitigate stragglers?

Tasks are encoded into redundant shares using linear secret sharing so that threshold-based recovery supports straggler mitigation and privacy preservation.

How is the offloading decision optimized and evaluated?

The paper formulates a privacy-aware problem that jointly optimizes delay and energy while penalizing theoretical privacy leakage from noisy leakage observations, solved via a branch-and-bound solver and a lightweight heuristic, evaluated with a discrete-event simulator.

Jul 16, 2026

Upload by Aurelia