Towards Enhanced Energy Aware Resource Optimization for Edge Devices Through Multi-cluster Communication Systems

Abstract

In the realm of the Internet of Things (IoT), the significance of edge devices within multi-cluster communication systems is on the rise. As the quantity of clusters and devices associated with each cluster grows, challenges related to resource optimization emerge. To address these concerns and enhance resource utilization, it is imperative to devise efficient strategies for resource allocation to specific clusters. These strategies encompass the implementation of load-balancing algorithms, dynamic scheduling, and virtualization techniques that generate logical instances of resources within the clusters. Moreover, the implementation of data management techniques is essential to facilitate effective data sharing among clusters. These strategies collectively minimize resource waste, enabling the streamlined management of networking and data resources in a multi-cluster communication system. This paper introduces an energy-efficient resource allocation technique tailored for edge devices in such systems. The proposed strategy leverages a higher-level meta-cluster heuristic to construct an optimization model, aiming to enhance the resource utilization of individual edge nodes. Emphasizing energy consumption and resource optimization while meeting latency requirements, the model employs a graph-based node selection method to assign high-load nodes to optimal clusters. To ensure fairness, resource allocation collaborates with resource descriptions and Quality of Service (QoS) metrics to tailor resource distribution. Additionally, the proposed strategy dynamically updates its parameter settings to adapt to various scenarios. The simulations confirm the superiority of the proposed strategy in different aspects.