Cloud-edge-end collaborative Artificial Intelligence (AI) computing requires schedulers that allocate heterogeneous resources for Directed Acyclic Graph (DAG)-structured workflows across network tiers. Cross-tier data transfers create ripple effects where a single placement decision propagates delays to downstream tasks, degrading end-to-end completion rates. This paper presents DREAM, a Dynamic Ripple-Effect-Aware Meta-scheduling scheme in which Critical Path Lookahead Scheduling (CPLS) performs bounded-depth trajectory planning with soft reservations for critical tasks, while Opportunity-Cost-Aware Placement (OCAP) evaluates non-critical tasks through a four-component cost covering immediate efficiency, ripple effect, load stability, and opportunity cost. Extensive simulations demonstrate that under the heavy load of 600 tasks, DREAM sustains a task completion rate of ~66%, exceeding classical heuristics by over 10 percentage points. At the extreme load of 1000 tasks, the system utility score improves by 47% over HEFT. Robustness experiments verify competitive performance across multiple DAG topologies and estimation-noise levels.
Chenlu Wang, Yuhuai Peng, Lei Liu et al.· IEEE Transactions on Cogniti...· 0 citations
Effective scaling of blockchain-enabled Industrial Internet of Things (IIoT) requires sharding that simultaneously ensures transaction locality, strict committee-size feasibility, and robustness against malicious node concentration. Existing methods often fail to balance this trilemma, risking either infeasible deployments or increased shard-takeover vulnerabilities. To address this, we propose GNN-OSS, a deployable sharding framework that decouples topology-aware preference learning from hard constraint enforcement. It first employs a trust-repulsion graph neural network to learn locality-aware preferences while discouraging low-trust nodes from collapsing into the same representation region. A Post-Hoc Capacity-Constrained Projection (PH-CCP) then maps these soft preferences into strictly feasible shard assignments. Finally, an entropy-driven Overlapping Sparse Scheme (OSS) selectively replicates boundary nodes to reduce residual cross-shard overhead without altering primary consensus membership. Evaluations demonstrate that, under the evaluated settings, GNN-OSS achieves a favorable performance–security trade-off. Against 20% malicious nodes, it substantially mitigates shard-takeover risks. Furthermore, it improves throughput by up to 33% over strictly feasible baselines and lowers the cross-shard ratio from 6.4% to 4.4% with minimal per-epoch overhead. Overall, GNN-OSS provides a practical sharding framework for open or hybrid blockchain-enabled IIoT environments.
Guangxia Xu, Zhuo Ye, Lu Wang et al.· IEEE Transactions on Network...· 0 citations
Satellite edge computing (SEC) has emerged as a promising paradigm to enhance in-orbit data processing capabilities and reduce transmission latency. However, satellite image processing tasks in SEC environments face critical challenges in efficient data handling, resource coordination, and transmission scheduling. The dynamic network topology and time-varying resource availability in satellite constellations further degrade the quality and stability of SEC services. To address these challenges, we propose a deep learning-based Collaborative Image Feature-extraction Task Optimization (CIFTO) framework. CIFTO dynamically distributes image processing workloads across multiple Low Earth Orbit (LEO) satellites, enabling continuous temporal updates for task allocation while significantly accelerating convergence and reducing computational overhead. By integrating temporal modeling and iterative optimization, CIFTO effectively mitigates the NP-hard nature of satellite task allocation. Furthermore, a lightweight satellite image processing model is designed to meet the strict constraints of on-orbit computation, achieving efficient image inference with minimal parameters. Extensive experimental evaluations demonstrate that the proposed framework ensures timely task completion, substantially lowers system-wide energy consumption, and enhances the adaptability and training efficiency of SEC services.
Xiaoteng Yang, Jie Feng, Lei Liu et al.· IEEE transactions on compute...· 0 citations