Software defined vehicular networks improve with smart control resizing
Enhancing SDVN Performance via Policy-Driven Lightweight Control-Plane Resizing Strategies
Networking and Internet Architecture
Summary
Vehicles communicating with roadside units often experience delays because the system controlling these communications can become overloaded. The authors propose two ways to adjust the size and workload of these control systems dynamically. One method reacts early to overload by shifting tasks away, and the other reacts when the communication quality lowers. Their tests show these methods reduce delays and lost messages compared to fixed setups.
What this means in practice
- •For vehicular network engineers: Optimize controller workload in multi-controller vehicle networks to maintain smooth and low-latency communication during high mobility and traffic.
- •For transportation system operators: Improve reliability of intelligent transportation systems by dynamically adjusting control resources based on network load or communication delays.
Authors
Muhammad Zain Ul Abideen, Prathapasinghe Dharmawansa, Nurul Huda Mahmood, Chafika Benzaïd
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.