Jul 2026· International Conference on Smart Communications and Networking· pp. 1-6· 0 citations· 12 references
Abstract
Task offloading is a key enabler for delay-sensitive Internet of Vehicles (IoV) services, where vehicular applications must be executed under strict latency constraints. This paper proposes a Proximal Policy Optimization (PPO)-based binary offloading framework that selects between Multi-access Edge Computing (MEC) and Cloud execution. Unlike purely simulation-based approaches, the proposed framework is built on a real-data-driven environment derived from vehicular mobility traces and measured service-delay observations collected from the Modena Automotive Smart Area (MASA) testbed. The RL agent observes mobility and delay-related features and learns a deadline-aware offloading policy through reward-driven interaction with the environment. Experimental results under a 50 ms deadline show that PPO achieves the best overall trade-off between task acceptance and delay control, while providing a more stable service behavior than fixed baselines.
This paper proposes a hierarchical computation framework that flexibly supports task execution across local vehicles, neighboring vehicles, RSUs, and cloud resources, and designs an efficient task migration and resource scheduling strategy that improves overall system performance under dynamic network conditions.
Liqun Yang· Journal of Grid Computing· 0 citations
Connected vehicles generate video, sensor, and bulk data that must be uploaded, cached, and forwarded across edge and cloud resources under short contact durations and congested backhaul. This paper studies selected data-management functions of a Smart Offloading Proxy (SOP) for 5G Internet of Vehicles (IoV): deadline-constrained scheduling of uploads already accepted at the edge proxy, and a radio-quality allocation signal. Control-plane functions are described but not evaluated; no end-to-end architecture validation is claimed. For proxy-side forwarding, six bandwidth-scheduling policies are formalized and evaluated in NS-3 against a first-come-first-served serve-one baseline. With bursty arrivals, a 60 Mbps bottleneck, and a 30 s dwell deadline, the shortest-remaining-k equal-allocation policy (SRK-EQ) is the strongest of the six scheduling policies, completing 92.5 ± 4.6% over 20 seeds versus 75.2 ± 12.0% for the all-jobs baseline; the serve-one baseline attains higher completion (98.0 ± 2.4%) for homogeneous 10 MB jobs. Under a heterogeneous 1/10/50 MB workload, SRK-EQ delivers lower latency (median 3.15 s versus 19.84 s) with overlapping completion estimates and a large-job fairness trade-off. In the tested replays, the Long Short-Term Memory (LSTM)-assisted configuration shows lower video and sensor delay with 38–48% lower mean per-flow video throughput than the baseline—a configuration-level latency-versus-throughput trade-off; the LSTM-specific effect is not isolated.
Latency-critical IoT applications, such as autonomous mobility and industrial automation, require deterministic guarantees to ensure that tasks are completed within strict deadlines. The 6G-enabled IoT-edge-cloud continuum can support such requirements by leveraging communication, computation and intelligence resources across devices, edge, and cloud infrastructures. However, existing task offloading strategies mainly focus on selecting where tasks are executed and typically assume immediate processing upon task arrival. This leads to transient congestion when multiple tasks coincide in time and results in inefficient resource utilization under dynamic workloads. This paper addresses these limitations by introducing an execution timing control strategy for deterministic task offloading that jointly determines where tasks are executed and when their execution starts, while guaranteeing deadline compliance. The key idea is to exploit the latency budget of tasks to control their execution timing, enabling a more balanced distribution of workload over time and reducing peak congestion across the continuum. Evaluation results show that, compared to existing benchmarks, the proposed approach achieves up to 70% higher satisfaction ratio, reduces the communication resources usage by 40%, lowers peak computing resource utilization by 15%, and decreases average execution time by up to 77%.
K. Aghababaiyan, B. Coll-Perales, Javier Gozálvez· 0 citations
Vehicular fog computing (VFC) enhances compute-intensive task processing by exploiting idle vehicle resources. However, existing offloading mechanisms may fail due to dynamic factors, such as vehicle mobility, unstable links, and service overload. This paper proposes an offloading-failure-aware (OFA) task offloading scheme (OFA-offloading). Although the exact offloading failure probability is difficult to obtain, it is determined by the service capability of the selected service vehicle (SV). Thus a new tractable metric, i.e., vehicle service capability (VSC), is defined to reflect the offloading failure probability, which is a function of vehicle mobility, resource availability, and link status. Based on VSC of each SV and considering that delay is important for VFC networks, an OFA delay utility is designed. Aiming to maximize this utility, a joint offloading SVs selection and computing resource allocation optimization problem is formulated. Since it is NP-hard and the VFC network is highly dynamic, a novel Graph Neural Network based federated Advantage Actor-Critic (GNN-FAC) algorithm is proposed to solve the problem. GNN-FAC can proactively predict environmental dynamics and incorporate VSC as a critical criterion for offloading decisions. Simulation results demonstrate that compared with existing offloading algorithms, OFA-offloading can improve the OFA delay utility by up to 40%.
Yihao Wu, Yanli Qi, Yiqing Zhou et al.· IEEE Transactions on Network...· 0 citations
With the emergence of autonomous vehicles and the ever-increasing volume of generated data, the Mobile Edge Computing paradigm has been proposed to address challenges related to latency and computational capacity. However, static vehicle-to-MEC association policies fail to meet these requirements due to the highly dynamic nature of vehicular networks. To address these challenges, this PhD research focuses on mobilityaware orchestration in MEC-enabled vehicular networks. In our first contribution, we introduce an ETSI-compliant proactive migration framework based on proximity-triggered migration notifications and a mobility-aware task migration strategy to relocate vehicular applications during mobility. The second contribution extends this work toward adaptive orchestration by formulating allocation and migration as a decision-making problem and developing a learning-based framework with a Simu5G–Python interface and a fairness- and delay-aware Maskable PPO agent. Results show improved response time, lower deadline miss rate, and balanced resource utilization under dense vehicular conditions. Our current work focuses on explainability methods to understand the learned policy and support trustworthy deployment while improving performance in terms of E2E delay, deadline miss rate, and fairness in resource utilization.
Rim Sayegh, Hela Marouane, Sahar Hoteit et al.· IEEE Conference on Network S...· 0 citations
The lack of determinism restricts the integration of safety-critical applications into Edge–Fog–Cloud (EFC) architectures. Existing EFC schedulers are typically designed for dynamic, best-effort operation based on unmanaged resource allocation and elastic virtualization. This paradigm introduces unbounded queueing, resource contention, and timing jitter, making standard schedulers unsuitable for hard-deadline workloads. Moreover, most approaches focus on computational placement, while communication is abstracted or treated as a secondary cost term. As a result, bounded-latency routing and deterministic task execution are rarely co-optimized under a unified timing model. This paper addresses these gaps by utilizing a managed Time-Triggered Edge–Fog–Cloud (TTEFC) architecture that supports safety-critical workloads, orchestrates IEEE Time-Sensitive Networking (TSN) for local intra-domain communication, and uses IETF Deterministic Networking (DetNet) for routed inter-domain paths. On this infrastructure, a hierarchical genetic algorithm (HGA) is proposed to jointly schedule partition-to-execution-location allocation, partition execution order, inter-partition route selection, and negotiated per-partition time budgets that act as temporal boundaries for parallel partition-level optimizers. An adaptive slack reallocation operator redistributes unused temporal slack from over-satisfied partitions to budget-violating partitions, improving feasibility convergence. Experiments on synthetic DAG workloads with 100–500 tasks compare the proposed HGA against HEFT and round-robin baselines. These baselines are included as scoped external references to contextualize the end-to-end scheduling performance of the proposed method. Ablation results show that slack reallocation improves partition-budget feasibility, reaches feasible budget assignments earlier, and produces tighter budget–makespan alignment than feedback-free and static-budget variants. An automotive-characteristic DAG case study further evaluates the method on an application-oriented workload under the same timing and communication assumptions.
Omar Hekal, Josepaul Paulachan, Daniel Onwuchekwa et al.· Future Internet· 0 citations