To address the issue of inefficient processing of computational task requests in heterogeneous Computing Power Network, a novel optimal pathfinding algorithm based on adap-tive task division is proposed. By modeling computing network resources and computational tasks as a Directed Acyclic Graph (DAG), a latency prediction model is established. This model, with latency as weights, facilitates adaptive task division within the DAG, and employs a reinforcement learning algorithm for optimal pathfinding. This approach matches computing nodes on the optimal path, enabling edge-to-edge computing and collabo-rative transmission, effectively reducing task processing latency. Simulation experiments demonstrate a significant reduction in the average system latency, with a maximum acceleration ratio of 5.8, showcasing improved performance in complex environments.
Task Scheduling Algorithm for Heterogeneous Computing Power Network
2024-06-24
1702400 byte
Conference paper
Electronic Resource
English
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