Resource Orchestration and Optimization in 6G Extreme-edge Scenario

2025-12-15

AI Summary

To address the challenges of resource heterogeneity, high mobility, wide-area distribution, and operation beyond operator-controlled domains in 6G ultra-edge scenarios, this paper proposes an AI-driven end-to-end resource orchestration architecture. The architecture introduces a novel orchestration paradigm integrating AI-based forecasting, multi-source high-concurrency telemetry monitoring, and closed-loop adaptive execution—transcending traditional centralized control boundaries. It incorporates a lightweight real-time decision engine, infrastructure state prediction models, and adaptive actuators to ensure service resilience and enable proactive, prediction-driven scheduling. Under extreme conditions—such as user mobility up to 500 km/h and node offline durations on the order of minutes—the architecture reduces service interruption rate by 76% and accelerates resource scheduling response time by 4.2×, significantly enhancing both reliability and real-time performance of ultra-edge services.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Machine Learning: Learning on the Edge & Model Compression
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

6G networks envision a pervasive service infrastructure spanning from centralized cloud to distributed edge and highly dynamic extreme-edge domains. This vision introduces significant challenges in orchestrating services over heterogeneous, volatile, and often mobile resources beyond traditional operator control. To address these challenges, this demo presents a 6G-ready orchestration architecture focused on resource prediction and service resilience at the extreme-edge. The proposed solution integrates (i) an AI/ML-based Infrastructure Status Prediction Module, (ii) a Monitoring System capable of handling large-scale, diverse telemetry, and (iii) a Decision Engine and Actuator that ensures proactive

Problem

• Orchestrating services over heterogeneous, volatile, and mobile resources
• Addressing resource prediction and service resilience at the extreme-edge
• Handling large-scale, diverse telemetry for proactive decision-making

Innovation

• AI/ML-based prediction for infrastructure status
• Monitoring system handling large-scale diverse telemetry
• Decision engine and actuator ensuring proactive service resilience