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首页> 外文期刊>Network Science and Engineering, IEEE Transactions on >Learning-Based Dynamic Resource Provisioning for Network Slicing with Ensured End-to-End Performance Bound
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Learning-Based Dynamic Resource Provisioning for Network Slicing with Ensured End-to-End Performance Bound

机译：保证端到端性能的网络切片基于学习的动态资源配置

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摘要

To accommodate different sets of network functions with different quality-of-service requirements for different types of applications in 5G networks, network slicing, which dynamically creates virtual networks, was proposed in the literature and IETF. A critical issue for network slicing is to determine the amount of resources for a network slice to ensure the quality-of-service requirement, and as such we need to determine the relationship among traffic demand, amount of resources, and end-to-end delay. This problem is non-trivial in a dynamic, virtualized environment. In this paper, we first use stochastic network calculus (SNC) to study the end-to-end delay bound with given traffic demand and resources. Then, we propose a solution to find the amount of resources that should be allocated with given traffic distribution and end-to-end delay bound. Beyond that, we investigate the range of traffic demands that a network slice can support and design a learning-based dynamic network slice resizing strategy, which can significantly reduce overall resizing cost with quality-of-service guarantee. Our work provides a set of useful tools for network slice tenants to (1) decide the amount of resources to request from physical network providers and (2) cost-effectively adjust the resource amounts that align with the dynamic traffic demand.
机译：为了适应5G网络中不同类型应用的不同网络功能集和不同服务质量要求，在文献和IETF中提出了动态创建虚拟网络的网络切片。网络切片的关键问题是确定网络切片的资源量以确保服务质量要求，因此，我们需要确定流量需求，资源量和端到端之间的关系延迟。在动态，虚拟化的环境中，这个问题并非易事。在本文中，我们首先使用随机网络演算（SNC）研究在给定流量需求和资源的情况下的端到端延迟范围。然后，我们提出一种解决方案，以找到应在给定流量分配和端到端延迟限制的情况下分配的资源量。除此之外，我们还研究了网络切片可以支持的流量需求范围，并设计了基于学习的动态网络切片调整大小策略，该策略可以通过服务质量保证显着降低总体调整大小成本。我们的工作为网络切片租户提供了一组有用的工具，以（1）确定从物理网络提供商处请求的资源量，（2）以符合成本效益的方式调整与动态流量需求匹配的资源量。

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