Automated Time-Series Prediction System Addresses Cold Start Problem in Cloud-Edge Computing
A team of researchers has published a preprint on arXiv introducing a fully automated time-series prediction system designed to manage resources across Cloud-Edge Continuum (CEC) infrastructure without requiring historical data from newly added nodes. The system combines a lightweight telemetry collector with a publicly released dataset called TimeTrack and a Neural Architecture Search engine to generate accurate predictive models from sparse local data. The work addresses a key barrier to Zero Touch Management in edge computing, where volatile, distributed environments make proactive resource orchestration difficult.
The paper, submitted to arXiv on June 8, 2026, targets the 'cold start' problem in Cloud-Edge Continuum environments, where newly discovered edge nodes lack the historical telemetry needed to train reliable local forecasting models. The authors introduce a Resource Exposer component that dynamically discovers nodes and collects customizable telemetry—including compute, network, and energy metrics—at a fine-grained level. To compensate for sparse initial local data, the framework automatically merges node-specific samples with TimeTrack, a publicly available high-resolution dataset sampled at 45-second intervals, which the authors also release as part of this work. A Neural Architecture Search engine then processes the merged data to produce optimized baseline models without manual intervention. Experimental results show that this data-mixing approach improves forecasting accuracy across MSE, MAE, and MAPE metrics and accelerates model convergence compared to training on local data alone, generic datasets alone, or alternative mixed datasets. The system is framed as a foundation for continuous MLOps deployment in latency-critical edge applications.
What's missing
The paper is a preprint and has not yet undergone peer review. Key limitations not discussed in the abstract include the generalizability of TimeTrack beyond the specific hardware and network environments in which it was collected, the computational overhead of the Neural Architecture Search engine on resource-constrained edge nodes, and the absence of real-world deployment results beyond experimental benchmarks. The scale and diversity of nodes used in experiments are also not described in the abstract.
What different sources said
- arXiv cs.LGCenter
Zero Touch Predictive Orchestration: Automating Time-Series Models for the Cloud-Edge Continuum
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