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

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AbstractMultiaccess 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.