Resource Slicing through Intelligent Orchestration of Energy-aware IoT services in Edge-Cloud Continuum
Hafiz Faheem Shahid, Erkki Harjula
TL;DR
The paper tackles energy-efficient deployment of IoT services in the edge-cloud continuum by leveraging semantic slicing in tandem with resource and network slicing. It proposes nanoservices—granular, lightweight components distributed across cloud, edge, and local nodes—to enable flexible, energy-aware orchestration. An Intelligent orchestration framework combines energy forecasting, QoS-aware resource allocation, and network slicing to place nanoservices on optimal nodes. Illustrative scenarios show energy trade-offs among local, edge, and cloud deployments under timing constraints, highlighting potential energy savings. The authors outline future work including AI-based performance evaluation in simulated and real-world setups linked to the 6G Flagship and Eware-6G initiatives.
Abstract
The rapid growth of the Internet of Things (IoT) applications inflicts high requirements for computing resources and network bandwidth. A growing number of service providers are applying edge-cloud computing to improve the quality of their services. Deploying IoT applications to optimal computing nodes to minimize energy consumption and enhance system performance remains an open challenge. In this paper, we present an intelligent orchestration concept for breaking down IoT applications into granular microservices, called nanoservices, and deploying them in an energy-aware manner to optimal computing nodes in the edge-cloud continuum by applying resource and network slicing methods. With this consolidated slicing scheme, we can efficiently allocate network and compute resources to meet the needs of these nanoservices.