2026· IEEE Transactions on Cognitive Communications and Networking· Vol 12, pp. 11341-11357· 0 citations· 39 references
Computer Science
Abstract
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.
G-STAR is a general graph-based scheduling framework that formalizes complex MAS pipelines as attributed Directed Acyclic Graphs (DAGs) and develops an industry-grade orchestration stack with asynchronous execution, resilient serving, and audit-friendly artifacts, offering a practical solution for optimizing web-scale...
Jiabao Song, Yunsheng Xia, Beibei Kong et al.· Proceedings of the 32nd ACM...· 0 citations
Results show that ECD-DVFS provides a balanced trade-off among performance, energy efficiency, reliability, cost, and QoS through adaptive and energy-aware workflow scheduling in hybrid fog–edge–cloud environments.
Zhihao Peng, Behnam Barzegar, Parmida Tavakoli et al.· Journal of Supercomputing· 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, res...
Omar Hekal, Josepaul Paulachan, Daniel Onwuchekwa et al.· Future Internet· 0 citations
Results provide initial evidence that multi-round CNP refinement is the principal protocol-level gain, with LLM assistance adding value for qualitative and uncertain runtime context.
This work study deadline-aware, mixed-criticality scheduling on heterogeneous MEC servers, where time-critical (TC) tasks must be protected at a controlled cost to best-effort traffic, and asks whether a multi-agent LLM control layer improves on a strong heuristic.
High-velocity workloads and intricate task dependencies inherent in distributed stream-processing systems pose a fundamental challenge to efficient resource allocation. Traditional heuristic and single-agent reinforcement learning (RL) schedulers frequently fail to recognize these complex network and data-flow interact...
Sunday J. Awine, Jinwei Liu· IEEE International Conferenc...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
