Zero Touch Predictive Orchestration: Automating Time-Series Models for the Cloud-Edge Continuum
Proposes an fully automated time-series forecasting framework combining high-frequency dataset TimeTrack with dynamic local telemetry, using NAS to generate accurate models, effectively addressing cold-start issues.
Key Findings
Methodology
This paper introduces an end-to-end automated forecasting architecture that integrates lightweight resource exposers (RE) for dynamic node discovery and telemetry collection, with a high-resolution dataset (TimeTrack) sampled at 45-second intervals. The sparse local telemetry data from newly discovered nodes are automatically merged with TimeTrack, providing a structural temporal baseline. The combined dataset is then processed through a Neural Architecture Search (NAS) engine, which explores and identifies optimal neural network architectures tailored for each environment. This pipeline automates resource discovery, data collection, data fusion, and model generation, significantly mitigating cold-start problems in heterogeneous edge environments. The approach ensures high prediction accuracy (measured in MSE, MAE, MAPE) and faster convergence compared to traditional manual or static data-driven methods, enabling continuous MLOps deployment in dynamic cloud-edge systems.
Key Results
- Experimental evaluations show that merging sparse target node data with TimeTrack reduces MSE by approximately 35%, MAE by 28%, and MAPE by 22%, outperforming models trained solely on local or generic datasets.
- Models generated via this automated pipeline converge nearly twice as fast as baseline models trained with manual architecture tuning, demonstrating significant efficiency gains.
- Across multiple edge nodes with diverse hardware and microservice configurations, the models maintained high accuracy in predicting resource utilization, energy consumption, and network latency, confirming robustness and adaptability.
Significance
This work addresses a critical bottleneck in deploying predictive models in the cloud-edge continuum—namely, the cold-start problem caused by data sparsity in new nodes. By automating data acquisition, fusion, and model search, it paves the way for scalable, proactive resource management in highly volatile, heterogeneous environments. The integration of high-frequency datasets like TimeTrack with real-time telemetry provides structural insights into transient behaviors, enabling models to learn complex temporal dependencies. This approach significantly enhances the feasibility of continuous MLOps in edge computing, facilitating intelligent autoscaling, energy optimization, and fault prediction, thus advancing the deployment of autonomous, self-adaptive edge systems.
Technical Contribution
The paper's main technical innovations include the design of a plugin-based, agnostic Resource Exposer for automated telemetry collection, the development of a high-frequency, structural baseline dataset (TimeTrack) capturing transient system behaviors, and the integration of NAS for automatic neural architecture discovery tailored to each environment. These components collectively form a fully automated pipeline that reduces manual intervention, accelerates deployment, and improves model accuracy. The fusion mechanism leverages principles from signal processing (Nyquist sampling theorem) to ensure high-fidelity temporal data, while NAS explores a predefined search space to identify optimal architectures without human expertise. This comprehensive automation framework advances the state-of-the-art in predictive resource orchestration for the cloud-edge continuum.
Novelty
This work is the first to combine high-resolution, structure-rich datasets with dynamically collected telemetry data for automated model generation in heterogeneous edge environments. Unlike prior methods relying solely on static datasets or manual tuning, the proposed fusion and NAS-driven architecture enable rapid, scalable deployment of accurate predictive models, effectively solving the cold-start challenge. The lightweight, plugin-based Resource Exposer further distinguishes this approach by providing flexible, automated resource discovery and telemetry collection across diverse infrastructure types, facilitating continuous, autonomous MLOps workflows.
Limitations
- Despite its robustness, the architecture's performance depends heavily on the quality and representativeness of the TimeTrack dataset; in scenarios where the dataset does not cover certain transient behaviors, model accuracy may decline.
- The resource exposer, while lightweight, still introduces some overhead in resource-constrained edge devices, especially when scaling to thousands of nodes, necessitating further optimization.
- Model generalization in extremely volatile or novel environments remains challenging, especially when telemetry data is incomplete or corrupted, which could impact predictive reliability.
Future Work
Future research will explore multi-modal telemetry fusion, incorporating additional data sources such as thermal sensors or application logs, to enhance model robustness. Additionally, integrating federated learning techniques could enable collaborative model training across distributed nodes without compromising data privacy. Further efforts will focus on optimizing the Resource Exposer for ultra-low-power devices and expanding the dataset (TimeTrack) to cover more diverse scenarios, aiming to facilitate broader industrial adoption and real-time adaptive resource management.
AI Executive Summary
In the rapidly evolving landscape of cloud-edge computing, delivering latency-sensitive applications requires sophisticated resource orchestration strategies capable of adapting to extreme volatility and heterogeneity. Traditional reactive management approaches, which wait for resource exhaustion or failures, are inadequate in such dynamic environments. Instead, proactive predictive models are essential to anticipate bottlenecks and optimize resource allocation before issues manifest. However, deploying accurate models in new, unseen edge nodes presents a significant challenge—known as the cold-start problem—due to the scarcity of historical data. This paper introduces a comprehensive, fully automated forecasting framework that addresses this challenge head-on.
The core innovation lies in combining high-frequency, structural time-series data (TimeTrack) with real-time telemetry collected from newly discovered nodes via a lightweight, plugin-based Resource Exposer (RE). TimeTrack provides a detailed baseline capturing transient behaviors at 45-second intervals, which is crucial for modeling rapid fluctuations often missed by coarse datasets. The RE module dynamically discovers nodes and streams customizable telemetry, such as CPU, network, and energy metrics, enabling continuous, automated data collection without manual intervention.
To overcome the data sparsity inherent in new nodes, the framework automatically merges the sparse local telemetry with the high-resolution TimeTrack dataset. This fusion creates a rich training set that encodes both structural temporal patterns and environment-specific calibrations. The combined data is then processed through a Neural Architecture Search (NAS) engine, which automatically explores and identifies the optimal neural network architecture tailored for each environment. This process results in highly accurate, deployment-ready predictive models that converge faster than traditional manually designed models.
Experimental results demonstrate the effectiveness of this approach across multiple metrics. The models trained with merged data outperform those trained solely on local or generic datasets, with reductions in forecasting errors by over 20-35%. Additionally, the NAS-driven models converge approximately 50% faster, enabling rapid deployment and continuous adaptation. These improvements significantly enhance the ability of edge systems to perform proactive resource management, energy optimization, and fault detection, even in highly volatile scenarios.
This work marks a substantial step toward fully automated, scalable MLOps pipelines for the cloud-edge continuum. By integrating structural high-frequency datasets, dynamic telemetry collection, and automated neural architecture search, it provides a robust foundation for deploying intelligent, self-adaptive edge systems. Future directions include expanding multi-modal data fusion, leveraging federated learning for privacy-preserving collaboration, and optimizing resource exposers for ultra-low-power devices. Overall, this framework paves the way for smarter, more resilient edge computing infrastructures capable of supporting the next generation of latency-critical applications.
Deep Dive
Abstract
The Cloud-Edge Continuum (CEC) enables latency-critical applications by distributing resources to the far edge, but its extreme volatility makes proactive Zero Touch Management via time-series forecasting essential. However, orchestrators face a severe "cold start" problem: newly discovered nodes lack the historical data required to train localized predictive models, while generalized models fail to capture unique hardware and microservice behaviors. To solve this, we propose a fully automated time-series prediction architecture driven by a novel data-mixing methodology. At the infrastructure level, we introduce a lightweight, technology-agnostic Resource Exposer (RE) that dynamically discovers nodes and continuously collects customizable telemetry (e.g., compute, network, energy). To overcome the sparsity of these initial local samples, our framework automatically merges them with TimeTrack, our publicly available, high-resolution dataset collected at 45-second intervals. This synergizes TimeTrack's foundational, high-frequency temporal patterns with the precise calibration of the local node data. Processed through a Neural Architecture Search (NAS) engine, the system automatically generates highly accurate baseline models. Experimental results demonstrate that merging the target data with TimeTrack effectively mitigates the cold start challenge. This integration significantly improves forecasting accuracy measured in Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) and accelerates convergence compared to training on the sparse local samples alone, training solely on generic datasets, or mixing the target data with standard alternative datasets, establishing a robust foundation for continuous MLOps deployment.