AI Research
DSTFView: Multi-View Cloud-Edge Workload Forecasting with Dual-Input Spatio-Temporal-Frequency Modeling
With the widespread deployment of edge-side AI inference, edge platforms are increasingly required to support latency-sensitive, highly concurrent, and reliability-critical applications. However, existing methods often struggle to balance multidimensional feature modeling and for...
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ArXiv CS.AI
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ArXiv CS.AI
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Accurate forecasting of cloud-edge workloads is crucial for optimizing resource allocation in latency-sensitive AI applications. DSTFView's multi-view spatio-temporal-frequency modeling offers a robust solution, outperforming existing methods by adaptively fusing different dependency perspectives.
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