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引用次数: 0

摘要

由于数据平面和控制平面的分离,软件定义网络(SDN)是5G的一个很有前途的技术。然而,特别是在超密集的场景中,由于SDN控制器的中心性,响应时间随着传入异构流的超高尖尖需求而增加。这种突然的增加导致端到端(e2e)延迟和控制器中的丢包率无法控制地上升。此外,这还会导致这种异构流管理中的QoS供应不平衡。为了应对这些挑战,本文提出了一种流感知QoS引擎,同时考虑了不同5G流(URLLC, eMBB, mMTC)的巨大流量需求和QoS需求。这种新颖的基于qos的引擎在单个控制器中包含两个步骤:准入管理和优先级管理。在允许管理中,我们通过实现包含不同流类型到达率的附加组件来改进基于损失率的通用随机早期检测算法(LRED)。在提出的优先级管理中,我们设计了一个基于树的优先级管理方案,我们根据公平性动态地为每个流类型分配优先级。根据我们全面的评估结果,在超密集SDN场景中,我们将三种不同的流的响应时间降低了53%,端到端延迟降低了58%,丢包率降低了36%。
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Flow-Aware QoS Engine for Ultra-Dense SDN Scenarios
Software-Defined Networking (SDN) is a promising technology for 5G thanks to the separation of data plane and control plane. However, especially in ultra-dense scenarios, as a result of the centrality of the SDN controller, the response time increases with the ultra high spiky demands of the in-coming heterogeneous flows. This sudden increase causes an uncontrollable rise in end-to-end (e2e) latency and drop rate in the controller. Moreover, this also leads to an unbalanced QoS provisioning in this heterogeneous flow management. To tackle these challenges, in this paper, we propose a Flow-Aware QoS Engine by considering both huge flow demands and QoS requirements of different 5G flows (URLLC, eMBB, mMTC). This novel QoS-based engine contains two steps in a single controller: The Admission Management and The Priority Management. In admission management, we modify the generic Loss Ratio-Based Random Early Detection Algorithm (LRED) by implementing an add-on containing the arrival rate of different flow types. In the proposed priority management, we design a tree-based priority management scheme where we dynamically assign priorities to each flow type regarding fairness. According to our thorough evaluation results, we get up to 53% lower response times, up to 58% lower e2e latencies, and up to 36% lower drop rates for three different flows in ultra-dense SDN scenarios.
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