Power Efficient Edge-Cloud Cooperation by Value-Sensitive Bayesian Attractor Model

Edge computing has emerged as a critical paradigm in AI and IoT applications, offering reduced latency and improved efficiency over traditional cloud-based systems. However, the limited computational resources at the edge and the need for energy-efficient operations pose significant challenges. In this paper, we propose a novel approach for edge-cloud co-operation using the Value-Sensitive Bayesian Attractor Model (VSBAM). Our method focuses on optimizing resource allocation and decision-making processes in edge computing environments, taking into account the computational constraints and power consumption requirements of both edge and cloud systems. Although the optimization of resource allocation is problematic in terms of its computation time, VSBAM reduces the computation time by performing the computation in a distributed manner for each session. Also, for the convergence problem in a distributed system, VSBAM can converge quickly by finding a quasi-optimal solution in a short period of time based on user values. Through simulation-based evaluations, we demonstrate that our approach can significantly reduce power consumption while maintaining high performance, especially in scenarios involving numerous sessions. Our findings also show that our decentralized control strategy is robust against power model errors and performs comparably to centralized control methods.