发表机构
Centre for Wireless Communications, University of Oulu（奥卢大学无线通信中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对SDVN中控制平面过载导致QoS下降的问题，提出两种轻量级控制平面调整机制，在Mininet-WiFi上验证，显著降低延迟和丢包并提升负载均衡。
AI 中文摘要
在高移动性和波动流量需求下的软件定义车载网络（SDVN）为延迟敏感的智能交通系统提供可编程的集中式控制。然而，由于频繁切换和密集的车对基础设施（V2I）接触，控制平面过载常常导致数据平面服务质量（QoS）下降。为解决此问题，我们提出了两种用于多控制器SDVN中低延迟控制平面调整的轻量级机制。第一种——控制平面中心控制平面调整机制——当预定义的负载阈值被超过时，主动将路边单元从过载控制器卸载到欠载或空闲控制器，以最小的决策延迟防止长时间过载。第二种——数据平面中心控制平面调整机制——基于可观察的数据平面QoS退化（如平均往返时间超过QoS阈值）触发调整，使控制平面适应与V2I服务体验对齐。两种机制均在Mininet-WiFi仿真测试平台上实现和评估，并采用现实中最坏情况下的车辆移动性。与固定的单控制器和静态多控制器基准相比，所提出的算法显著降低了端到端延迟和数据包丢失，同时提高了负载均衡率。
英文摘要
Software-defined vehicular networks (SDVNs) under high mobility and fluctuating traffic demand offer programmable, centralized control for latency-sensitive intelligent transportation systems. However, data-plane Quality of Service (QoS) is often degraded by control-plane overload due to frequent handovers and dense vehicle-to-infrastructure (V2I) contacts. To address this, we propose two lightweight mechanisms for low-latency control-plane resizing in multi-controller SDVNs. The first - \textit{Control-plane Centric Control-plane Resizing Mechanism} - proactively offloads roadside units from overloaded controllers to underloaded or idle ones when a predefined load threshold is exceeded, preventing prolonged overload with minimal decision latency. The second - \textit{Data-plane Centric Control-plane Resizing Mechanism} - triggers resizing based on observable data-plane QoS degradation, such as average round-trip time exceeding a QoS threshold, aligning control-plane adaptation with V2I service experience. Both mechanisms are implemented and evaluated on Mininet-WiFi emulation testbeds with realistic worst-case vehicles mobility. Compared to fixed single-controller and static multi-controller benchmarks, the proposed algorithms significantly reduce end-to-end delay and packet loss while improving load balancing rate.
