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Optimal Distributed Resource Allocation in 5G Virtualized Networks

机译:5G虚拟网络中的最佳分布式资源分配

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The concepts of network function virtualization (NFV) and end-to-end (E2E) network slicing are two promising technologies empowering 5G networks for efficient, flexible and dynamic network deployment and service management. Optimal resource allocation is one of the challenging problems to address in such networks. In this paper, we propose a resource allocation model for 5G virtualized networks in a heterogeneous cloud infrastructure. In our model, each network slice has a resource demand vector for each of its building virtual network functions (VNFs). We then formulate the optimal resource allocation as a convex optimization problem maximizing the overall system utility as a function of the slice thicknesses with the constraints of the data centers' resource capacities. The slice thickness variables together with the demand vectors determine the amount of resources allocated to each slice. We further propose a distributed solution for the resource allocation problem based on auction/game theory by forming a resource auction between the slices and the data centers (DCs). It is shown that the resource allocation game has a unique Nash equilibrium and its solution is the same as the solution of the centralized system optimization problem, i.e., in equilibrium the slice thicknesses maximize the overall system utility. Numerical analysis are provided to show the validity of the results, evaluate the convergence of the distributed solution and also comparing the performance of the optimal scheme with heuristic ones.
机译:网络功能虚拟化(NFV)和端到端(E2E)网络切片的概念是两项有前途的技术,可为5G网络提供有效,灵活和动态的网络部署和服务管理能力。最佳的资源分配是在此类网络中要解决的挑战性问题之一。在本文中,我们提出了异构云基础架构中5G虚拟网络的资源分配模型。在我们的模型中,每个网络切片都有一个针对其建筑虚拟网络功能(VNF)的资源需求向量。然后,我们将最佳资源分配公式化为凸优化问题,从而使整个系统效用最大化(取决于切片厚度),并受到数据中心资源容量的限制。切片厚度变量与需求向量一起确定分配给每个切片的资源量。通过在切片和数据中心(DC)之间形成资源拍卖,我们进一步提出了基于拍卖/博弈理论的资源分配问题的分布式解决方案。结果表明,资源分配博弈具有唯一的纳什均衡,其解决方案与集中式系统优化问题的解决方案相同,即在均衡状态下,切片厚度最大化了整个系统的效用。数值分析表明了结果的有效性,评估了分布式解的收敛性,并将最优方案的性能与启发式方案进行了比较。

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