Age-of-Information and Energy Optimization in Digital Twin Edge Networks

Yongna Guo, Yaru Fu, Yan Zhang, Tony Q. S. Quek
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Abstract

In this paper, we study the intricate realm of digital twin synchronization and deployment in multi-access edge computing (MEC) networks, with the aim of optimizing and balancing the two performance metrics Age of Information (AoI) and energy efficiency. We jointly consider the problems of edge association, power allocation, and digital twin deployment. However, the inherent randomness of the problem presents a significant challenge in identifying an optimal solution. To address this, we first analyze the feasibility conditions of the optimization problem. We then examine a specific scenario involving a static channel and propose a cyclic scheduling scheme. This enables us to derive the sum AoI in closed form. As a result, the joint optimization problem of edge association and power control is solved optimally by finding a minimum weight perfect matching. Moreover, we examine the one-shot optimization problem in the contexts of both frequent digital twin migrations and fixed digital twin deployments, and propose an efficient online algorithm to address the general optimization problem. This algorithm effectively reduces system costs by balancing frequent migrations and fixed deployments. Numerical results demonstrate the effectiveness of our proposed scheme in terms of low cost and high efficiency.
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数字双子边缘网络中的信息时代与能源优化
本文研究了多接入边缘计算(MEC)网络中数字孪生同步和部署的复杂领域,旨在优化和平衡信息时代(AoI)和能效这两个性能指标。我们共同考虑了边缘关联、功率分配和数字孪生部署等问题。然而,该问题固有的随机性给确定最佳解决方案带来了巨大挑战。为了解决这个问题,我们首先分析了优化问题的可行性条件。然后,我们研究了一个涉及静态信道的特定场景,并提出了一种循环调度方案。这样,我们就能以封闭形式推导出 AoI 总和。因此,通过寻找最小权重的完美匹配,边缘关联和功率控制的联合优化问题得到了优化解决。此外,我们还研究了频繁数字孪生迁移和固定数字孪生部署情况下的单次优化问题,并提出了一种高效的在线算法来解决一般优化问题。该算法通过平衡频繁迁移和固定部署,有效降低了系统成本。数值结果证明了我们提出的方案在低成本和高效率方面的有效性。
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