Optimizing AI-driven Geographic Simulation Task Scheduling through Intelligent Runtime Estimation for Distributed Heterogeneous Clusters
Mar 10, 2026·,,
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1 min read
Wanhao Li
Min Chen
Fengyuan Zhang
Peilong Ma
Zaiyang Ma
Yongning Wen
Songshan Yue
Guonian Lu
Abstract
This work proposes an AI-driven task scheduling optimization framework for geo-simulation based on intelligent runtime estimation. It integrates real-time resource monitoring, historical task knowledge, and large language model prediction to support adaptive, resource-aware scheduling in distributed heterogeneous clusters.
Type
Publication
SSRN preprint, under review
This under-review work studies how runtime estimation and resource-aware scheduling can improve throughput and balance for AI-driven geo-simulation tasks on distributed heterogeneous clusters.
