TriCloudEdge: A multi-layer Cloud Continuum

TL;DR

TriCloudEdge presents a scalable multi-layer cloud continuum architecture integrating edge and cloud resources, enabling efficient AI processing, data transfer, and resource management across tiers with diverse protocols.

Contribution

The paper introduces a novel three-tier cloud continuum architecture that supports multi-protocol data transfer and demonstrates trade-offs between resource use and communication efficiency.

Findings

TriCloudEdge effectively distributes computational tasks to reduce latency.

The architecture balances resource utilization with communication costs.

AI model adaptation is feasible at the far edge with manageable computational effort.

Abstract

TriCloudEdge is a scalable three-tier cloud continuum that integrates far-edge devices, intermediate edge nodes, and central cloud services, working in parallel as a unified solution. At the far edge, ultra-low-cost microcontrollers can handle lightweight AI tasks, while intermediate edge devices provide local intelligence, and the cloud tier offers large-scale analytics, federated learning, model adaptation, and global identity management. The proposed architecture enables multi-protocols and technologies (WebSocket, MQTT, HTTP) compared to a versatile protocol (Zenoh) to transfer diverse bidirectional data across the tiers, offering a balance between computational challenges and latency requirements. Comparative implementations between these two architectures demonstrate the trade-offs between resource utilization and communication efficiency. The results show that TriCloudEdge can distribute computational challenges to address latency and privacy concerns. The work also presents tests of AI model adaptation on the far edge and the computational effort challenges under the prism of parallelism. This work offers a perspective on the practical continuum challenges of implementation aligned with recent research advances addressing challenges across the different cloud levels.