Automated Time-Series Prediction System Addresses Cold Start Problem in Cloud-Edge Computing
Researchers propose a fully automated architecture that uses data-mixing to help newly discovered nodes in cloud-edge systems generate accurate predictive models without extensive historical data. The system combines local telemetry with a public high-resolution dataset called TimeTrack and uses Neural Architecture Search to automatically create baseline models. This approach is significant because it enables proactive resource management in latency-critical distributed systems where traditional model training methods fail due to sparse initial data.
The paper introduces a solution to the "cold start" problem in cloud-edge computing, where newly discovered nodes lack sufficient historical data to train localized predictive models for resource orchestration. The proposed framework includes a lightweight Resource Exposer that dynamically discovers nodes and collects customizable telemetry data at infrastructure level, combined with a novel data-mixing methodology that merges sparse local samples with TimeTrack, a publicly available high-resolution dataset collected at 45-second intervals. A Neural Architecture Search engine automatically processes this merged data to generate accurate baseline models. Experimental results show that this integration significantly improves forecasting accuracy across multiple metrics (MSE, MAE, MAPE) and accelerates model convergence compared to training solely on local data, generic datasets, or standard alternative datasets. The technology-agnostic design supports continuous MLOps deployment in the Cloud-Edge Continuum.
What's missing
The paper does not provide details on computational overhead of the Neural Architecture Search engine, scalability testing across different numbers of nodes, or comparison with other cold-start mitigation techniques from recent literature. The study's limitations regarding the generalizability of TimeTrack patterns to significantly different hardware architectures or geographic regions are not explicitly discussed.
What different sources said
- arXiv cs.LGCenter
Zero Touch Predictive Orchestration: Automating Time-Series Models for the Cloud-Edge Continuum
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