EES-CND: Collaborative Neural Decision-Making for Drift-Aware Fault-Tolerant Edge-Cloud Service Placement

2026-06-01

AI Summary

In edge-cloud environments, hardware failures, software faults, and dynamic operating conditions frequently lead to service disruptions and performance degradation, jeopardizing system reliability and service-level objectives. This work proposes a lightweight collaborative neural decision framework that employs multiple compact neural networks to jointly infer fault-tolerant service redeployment strategies. The framework integrates an enhanced evolutionary strategy to enable online, adaptive model updates. By effectively addressing dynamic faults and performance drift, the approach ensures rapid service recovery and maintains responsive performance while substantially enhancing system reliability. Experimental results demonstrate a 44.8% reduction in fault-tolerance overhead compared to single-model solutions.

Abstract

The edge-cloud paradigm improves service delivery by orchestrating resources across edge nodes and cloud data centres. These environments consist of heterogeneous, interconnected computing nodes that cooperate to deliver continuous services. However, their scale and complexity increase vulnerability to failures from hardware malfunctions, software defects, and dynamic operating conditions. These failures can disrupt system configurations and service execution, leading to reduced reliability, performance degradation, and violations of service-level objectives. Ensuring service execution requires adaptive service placement strategies across edge-cloud resources. This study introduces a fault-tolerant service placement approach (Enhanced Evolution Strategy for Collaborative Neural Decision-making, EES-CND) for edge-cloud environments. The method employs collaborative decision-making, wherein multiple lightweight neural networks jointly infer redeployment strategies during failure events. To address the system dynamics and mitigate performance drift, adaptive models are updated online using an enhanced evolution strategy. Extensive simulations show that EES-CND effectively handles performance drift and significantly outperforms existing methods in service recovery time, response time, and reliability, achieving a 44.8\% reduction in fault-tolerance cost compared to standalone models.

Problem

• fault-tolerant
• service placement
• edge-cloud
• performance drift
• system reliability

Innovation

• collaborative neural decision-making
• enhanced evolution strategy
• drift-aware
• fault-tolerant service placement
• edge-cloud computing