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Workflow Makespan Minimization for Partially Connected Edge Network: A Deep Reinforcement Learning-Based Approach
2022
IEEE Open Journal of the Communications Society
The ever advances in wireless communication and mobile networks have brought novel workflow-formed applications, such as virtual reality and live-streaming, to our daily life. Arousing a growing need for workflow execution efficiency. An edge network is widely considered a promising way of bridging the gap between intensive resource demand and the limited computation capabilities of mobile terminals. However, when an edge network is partially connected, ordinary workflow scheduling algorithms
doi:10.1109/ojcoms.2022.3158417
fatcat:5nv4ylxurrc2neqk4jmayza6ya