Distributed Digital Twin Migration in Multi-Tier Computing Systems
Distributed Digital Twin Migration in Multi-Tier Computing Systems
复制标题
多层计算系统中的分布式数字孪生迁移
DOI：
10.1109/jstsp.2024.3359009
复制
发表时间：
2024-01
影响因子：
7.5
通讯作者：
Zhixiong Chen;Wenqiang Yi;Arumgam Nallanathan;Jonathon A. Chambers
中科院分区：
文献类型：
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作者：
Zhixiong Chen;Wenqiang Yi;Arumgam Nallanathan;Jonathon A. Chambers
在网络边缘，多层计算框架为移动的用户提供高效的云计算和信号处理能力。在多层计算系统中部署数字双胞胎有助于实现用户与其虚拟对象之间的超可靠和低延迟交互。考虑到系统中的用户可能会在覆盖范围有限的边缘服务器之间漫游，并增加其数字双胞胎的数据同步延迟，因此解决数字双胞胎迁移问题以实现数字双胞胎和用户之间的实时同步至关重要。为此，我们提出了一个联合的数字孪生迁移、通信和计算资源管理问题，以最大限度地减少数据同步延迟，其中考虑了时变网络状态和用户移动性。通过在确定性迁移策略下解耦边缘服务器，我们首先使用凸优化方法推导出每个服务器的最佳通信和计算资源管理策略。对于不同服务器之间的数字孪生迁移问题，我们将其转化为一个分散的部分可观察马尔可夫决策过程（Dec-POMDP）。为了解决这个问题，提出了一种新的基于Agent贡献的多Agent强化学习（AC-MARL）算法，该算法采用反事实基线方法来表征每个Agent的贡献，促进Agent之间的协作，从而实现用户的分布式数字孪生迁移.此外，我们利用嵌入矩阵来编码代理的动作和状态，以缓解AC-MARL在高维状态下的可扩展性问题。基于两个真实出租车移动轨迹数据集的仿真结果表明，与基准方案相比，提出的数字孪生迁移方案能够减少23%-30%的用户数据同步延迟。
At the network edges, the multi-tier computing framework provides mobile users with efficient cloud-like computing and signal processing capabilities. Deploying digital twins in the multi-tier computing system helps to realize ultra-reliable and low-latency interactions between users and their virtual objects. Considering users in the system may roam between edge servers with limited coverage and increase the data synchronization latency to their digital twins, it is crucial to address the digital twin migration problem to enable real-time synchronization between digital twins and users. To this end, we formulate a joint digital twin migration, communication and computation resource management problem to minimize the data synchronization latency, where the time-varying network states and user mobility are considered. By decoupling edge servers under a deterministic migration strategy, we first derive the optimal communication and computation resource management policies at each server using convex optimization methods. For the digital twin migration problem between different servers, we transform it as a decentralized partially observable Markov decision process (Dec-POMDP). To solve this problem, we propose a novel agent-contribution-enabled multi-agent reinforcement learning (AC-MARL) algorithm to enable distributed digital twin migration for users, in which the counterfactual baseline method is adopted to characterize the contribution of each agent and facilitate cooperation among agents. In addition, we utilize embedding matrices to code agents' actions and states to release the scalability issue under the high dimensional state in AC-MARL. Simulation results based on two real-world taxi mobility trace datasets show that the proposed digital twin migration scheme is able to reduce 23%–30% data synchronization latency for users compared to the benchmark schemes.