Resource Orchestration and Optimization in 6G Extreme-edge Scenario

TL;DR

This paper proposes a 6G orchestration architecture for extreme-edge scenarios, emphasizing resource prediction and resilience through AI/ML, large-scale monitoring, and proactive decision-making.

Contribution

It introduces a novel architecture integrating AI/ML-based prediction, large-scale telemetry, and proactive decision mechanisms for 6G extreme-edge resource management.

Findings

Effective resource prediction at the edge

Enhanced service resilience through proactive orchestration

Scalable monitoring system for diverse telemetry

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