Adaptive power management for multiaccess edge computing-based 6G-inspired massive Internet of Things

Abstract

Multiaccess edge computing (MEC) is a dynamic approach for addressing the capacity and ultra-latency demands caused by the pervasive growth of real-time applications in next-generation (xG) wireless communication networks. Powerful computational resource-enriched virtual machines (VMs) are used in MEC to provide outstanding solutions. However, a major challenge with using VMs in xG networks is the high overhead caused by the excessive energy demands of VMs. To address this challenge, containers, which are generally more energy-efficient and less computationally demanding, are being advocated. This paper proposes a containerised edge computing model for power optimisation in 6G-inspired massive Internet-of-Things applications. The problem is formulated as a central processing unit energy consumption cost function based on quasi-finite system observations. To achieve practicable computational complexity, an approach that uses a search heuristic based on Lyapunov techniques is employed to obtain near-optimal solutions. Important performance metrics are successfully predicted using the online look-ahead technique. The predictive model used achieves an accuracy of 97% prediction compared to actual data. To further improve resource demand, an adaptive controller is used to schedule computational resources on a time slot basis in an adaptive manner while continuing to receive workload levels to plan future resource provisioning. The proposed technique is shown to perform better compared to a competitive baseline algorithm.