An exact continuous-time MILP approach for multi-objective IoT workflow scheduling in edge-hub-cloud systems achieves better latency, energy, and reliability than heuristics with practical runtimes.
A self-supervised deep reinforcement learning for zero-shot task scheduling in mobile edge computing environments
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.DC 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Exact, Efficient, and Reliable Multi-Objective and Multi-Constrained IoT Workflow Scheduling in Edge-Hub-Cloud Cyber-Physical Systems
An exact continuous-time MILP approach for multi-objective IoT workflow scheduling in edge-hub-cloud systems achieves better latency, energy, and reliability than heuristics with practical runtimes.