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State Management Function Placement for Service-Based 5G Mobile Core Architecture

机译:基于服务的5G移动核心架构的状态管理功能放置

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Service-based 5G core architecture is designed to take advantages of network function virtualization and software-defined networking. In addition to the control and data plane separation, the service-based 5G core decouples the computing and storage resources which separates the mobile functions into two categories: "stateless" control functions and state management functions. Such new features improve 5G core network in terms of independent scalability and fast failure recovery. In geo-distributed cloud infrastructure-based 5G core networks, the "stateless" control functions can be deployed to all cloud centers close to access networks to reduce latency and traffic load burden. However, we can not deploy state management functions to all cloud centers which results in the high state transfer cost. In addition, we can not use only one state management function for entire network which results in high traffic load burden. Therefore, the placement of state management functions involves different conflicting design objectives which requires a new model to optimally place these functions. In this paper, we propose a multi-objective model which can achieve the balance between state transfer cost and traffic load burden on state management functions. We first solve our model using epsilon - constraint approach which tries to optimize one objective while keeping another under threshold. Second, we propose an adaptive solution based on adaptive weighted sum approach to find a set of Pareto optimal solutions for our multi-objective model. Simulation results show that our proposed solution offers better balance between two design objectives compared to other solutions.
机译:基于服务的5G核心架构旨在利用网络功能虚拟化和软件定义网络的优势。除了控制和数据平面分离之外,基于服务的5G核心还使计算和存储资源分离,从而将移动功能分为两类:“无状态”控制功能和状态管理功能。这些新功能在独立可扩展性和快速故障恢复方面改善了5G核心网络。在基于地理分布的基于云基础架构的5G核心网络中,“无状态”控制功能可以部署到靠近接入网络的所有云中心,以减少延迟和流量负载负担。但是,我们无法将状态管理功能部署到所有云中心,这会导致较高的状态转移成本。另外,我们不能仅对整个网络使用一种状态管理功能,这会导致高流量负载负担。因此,状态管理功能的放置涉及不同的冲突设计目标,这就需要一种新的模型来最佳地放置这些功能。在本文中,我们提出了一个多目标模型,该模型可以在状态转移成本和交通负荷对状态管理功能的负担之间取得平衡。我们首先使用epsilon-约束方法求解模型,该方法试图优化一个目标,同时将另一个目标保持在阈值以下。其次,我们提出了一种基于自适应加权和方法的自适应解,为我们的多目标模型找到了一组帕累托最优解。仿真结果表明,与其他解决方案相比,我们提出的解决方案在两个设计目标之间提供了更好的平衡。

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