Zero Touch Predictive Orchestration: Automating Time-Series Models for the Cloud-Edge Continuum
arXiv:2606.09787v1 Announce Type: new 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
The proliferation of edge computing devices and the need for low-latency AI applications are driving demand for more adaptive and autonomous orchestration solutions to manage distributed resources effectively.
This development addresses a critical challenge in distributed AI systems by enabling proactive resource management at the edge, which is essential for scaling complex AI applications and maintaining operational efficiency in dynamic environments.
Traditional manual or centrally dependent orchestration methods will gradually be replaced by self-learning, automated systems that can adapt to changing conditions and new deployments without human intervention.
- · Cloud-edge infrastructure providers
- · Companies deploying AI at the edge
- · AI orchestration software developers
- · Telecommunications providers
- · Legacy manual IT operations teams
- · Companies without robust edge AI strategies
Increased reliability and efficiency of distributed AI applications across the Cloud-Edge Continuum.
Accelerated adoption of diverse AI use cases demanding real-time responsiveness at the network edge.
Reduced operational costs and energy consumption for large-scale distributed AI deployments, potentially enabling new service models.
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Read at arXiv cs.LG