Cognitive Load Balancing in AI-Enhanced Microservices for Edge–Cloud Continuum Systems
Keywords:
cognitive computing, load balancing, edge computing, AI microservices, cloud orchestration, , distributed systems, edge–cloud continuum, container migration, real-time computingAbstract
Purpose: This paper investigates the integration of cognitive load balancing techniques in AI-enhanced microservices within edge–cloud continuum systems, aiming to improve resource utilization, system responsiveness, and resilience.
Design/methodology/approach: We propose a framework that combines cognitive AI decision-making with dynamic load balancing to handle fluctuating workloads in distributed edge-cloud environments. The study evaluates key architectural patterns and orchestration mechanisms across the edge-cloud continuum.
Findings: Cognitive load balancing improves system adaptability by reducing latency, avoiding resource bottlenecks, and enhancing energy efficiency. AI-driven orchestration enables microservices to migrate and scale based on workload predictions and edge-cloud resource states.
Practical implications: The results can guide the design of resilient, self-adaptive microservice systems for smart cities, industrial IoT, and intelligent transportation where latency and scalability are critical.
Originality/value: This work uniquely integrates cognitive AI with microservice orchestration in a multi-layer edge–cloud architecture and presents validated optimization strategies for dynamic load distribution.
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Copyright (c) 2023 Ian McEwan du Maurier, (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
