Problem
Research questions and friction points this paper is trying to address.
Software-defined vehicular networks
Quality of Service
Control-plane overload
Frequent handovers
Vehicle-to-infrastructure contacts
Enhancing SDVN Performance via Policy-Driven Lightweight Control-Plane Resizing Strategies
🤖 AI Summary
为解决SDVN中因频繁切换和密集V2I联系导致的控制平面过载问题，提出两种轻量级机制动态调整控制平面大小，从而减少延迟和丢包。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Lightweight Control-Plane Resizing
Software-Defined Vehicular Networks (SDVNs)
Quality of Service (QoS)
🔎 Similar Papers
No similar papers found.
