Optimizing Task Offloading and Resource Management in 6G Networks Through a Hierarchical Edge-Fog-Cloud Architecture

Abstract

The high-speed development of 6G technology demands novel network architectures that can manage increasing volumes of data and connected devices efficiently. This paper introduces a novel hierarchical edge-fog-cloud (HEFC) architecture in resource allocation and energy efficiency optimized for 6G networks. The next generation of communication should be delivered by 6G technology that will make the design of interaction between humans, data, and devices. Because of requirements in 6G, HEFC architecture dynamically allocates computational tasks to HEFC depending on complexity latency requirements. The edge layer is responsible for most latency-sensitive tasks. However, the current usage of edge and fog computing models takes into account only the single-point optimization. The proposed HEFC model achieves a low latency of 8 ms, an energy consumption of 110 kWh, a high throughput of 600 tasks/s, and an efficient resource utilization of 85%. The main aim of our study is to design a solid, robust and scalable HEFC architecture that can tackle the challenges of task offloading, resource allocation and energy consumption for the 6G systems. The approach employs dynamic voltage and frequency scaling (DVFS) with advanced optimization methods to enhance energy efficiency and scalability in the network core. By efficiently allocating workloads, the proposed EFC model excels existing architectures in important performance areas. This paper presents sustainable 6G network model to tackle the challenge of latency-sensitive and energy-greedy applications. The results can guide future network designs towards more efficient and adaptive architectures, making 6G networks more responsive and dependable to evolving technological demands.