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无线网络切片中用于服务质量差异化的切片管理

Slice Management for Quality of Service Differentiation in Wireless Network Slicing.

作者信息

An Namwon, Kim Yonggang, Park Juman, Kwon Dae-Hoon, Lim Hyuk

机构信息

School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea.

Agency for Defense Development, Daejeon 34186, Korea.

出版信息

Sensors (Basel). 2019 Jun 19;19(12):2745. doi: 10.3390/s19122745.

DOI:10.3390/s19122745
PMID:31248088
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6630757/
Abstract

Network slicing is a technology that virtualizes a single infrastructure into multiple logical networks (called slices) where resources or virtualized functions can be flexibly configured by demands of applications to satisfy their quality of service (QoS) requirements. Generally, to provide the guaranteed QoS in applications, resources of slices are isolated. In wired networks, this resource isolation is enabled by allocating dedicated data bandwidths to slices. However, in wireless networks, resource isolation may be challenging because the interference between links affects the actual bandwidths of slices and degrades their QoS. In this paper, we propose a slice management scheme that mitigates the interference imposed on each slice according to their priorities by determining routes of flows with a different routing policy. Traffic flows in the slice with the highest priority are routed into shortest paths. In each lower-priority slice, the routing of traffic flows is conducted while minimizing a weighted summation of interference to other slices. Since higher-priority slices have higher interference weights, they receive lower interference from other slices. As a result, the QoS of slices is differentiated according to their priorities while the interference imposed on slices is reduced. We compared the proposed slice management scheme with a naïve slice management (NSM) method that differentiates QoS among slices by priority queuing. We conducted some simulations and the simulation results show that our proposed management scheme not only differentiates the QoS of slices according to their priorities but also enhances the average throughput and delay performance of slices remarkably compared to that of the NSM method. The simulations were conducted in grid network topologies with 16 and 100 nodes and a random network topology with 200 nodes. Simulation results indicate that the proposed slice management increased the average throughput of slices up to 6%, 13%, and 7% and reduced the average delay of slices up to 14%, 15%, and 11% in comparison with the NSM method.

摘要

网络切片是一种将单一基础设施虚拟化为多个逻辑网络(称为切片)的技术,在这些网络中,可以根据应用程序的需求灵活配置资源或虚拟功能,以满足其服务质量(QoS)要求。通常,为了在应用程序中提供有保障的QoS,切片的资源是隔离的。在有线网络中,通过为切片分配专用数据带宽来实现这种资源隔离。然而,在无线网络中,资源隔离可能具有挑战性,因为链路之间的干扰会影响切片的实际带宽并降低其QoS。在本文中,我们提出了一种切片管理方案,该方案通过使用不同的路由策略确定流的路由,根据切片的优先级减轻施加在每个切片上的干扰。具有最高优先级的切片中的业务流被路由到最短路径。在每个较低优先级的切片中,进行业务流的路由,同时将对其他切片的干扰加权总和降至最低。由于较高优先级的切片具有较高的干扰权重,它们受到来自其他切片的干扰较低。结果,切片的QoS根据其优先级进行区分,同时减少了施加在切片上的干扰。我们将提出的切片管理方案与一种简单的切片管理(NSM)方法进行了比较,该方法通过优先级排队来区分切片之间的QoS。我们进行了一些模拟,模拟结果表明,我们提出的管理方案不仅根据切片的优先级区分了切片的QoS,而且与NSM方法相比,显著提高了切片的平均吞吐量和延迟性能。模拟是在具有16个和100个节点的网格网络拓扑以及具有200个节点的随机网络拓扑中进行的。模拟结果表明,与NSM方法相比,提出的切片管理使切片的平均吞吐量提高了6%、13%和7%,并将切片的平均延迟降低了14%、15%和11%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/f88cddc6dc5a/sensors-19-02745-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/4b8bcbfa6c2f/sensors-19-02745-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/08ebb8f00294/sensors-19-02745-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/2b070eeb9b99/sensors-19-02745-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/e4355d6b2156/sensors-19-02745-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/3dfd1ffdd5b8/sensors-19-02745-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/5257bbd012b2/sensors-19-02745-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/ec22f6a512c9/sensors-19-02745-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/4f35f83899e2/sensors-19-02745-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/f88cddc6dc5a/sensors-19-02745-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/4b8bcbfa6c2f/sensors-19-02745-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/08ebb8f00294/sensors-19-02745-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/2b070eeb9b99/sensors-19-02745-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/e4355d6b2156/sensors-19-02745-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/3dfd1ffdd5b8/sensors-19-02745-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/5257bbd012b2/sensors-19-02745-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/ec22f6a512c9/sensors-19-02745-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/4f35f83899e2/sensors-19-02745-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e84/6630757/f88cddc6dc5a/sensors-19-02745-g009.jpg

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