Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.35585

Resource versus Responsiveness: Benchmarking SDN Controller Runtimes for a Moving-Target-Defense Control Plane at Scale

Souhail Chakkour·Umesh Biswas·Charan Gudla
View original source ↗Read source document ↗Export metadata ↓

Abstract

Network Moving Target Defense (MTD) built on Software-Defined Networking (SDN) continuously rotates host-facing addresses to invalidate an attacker's reconnaissance. Each rotation creates a burst of control-plane mutations, while new connections may simultaneously require reactive flow installation. Yet SDN controller runtime is usually treated as an implementation detail in the MTD literature. We show that it materially affects performance. We port the same Continuity-Preserving Address Mutation (CPAM) logic to three widely used controllers: Ryu (single-threaded cooperative Python), OpenDaylight, and ONOS (both multi-threaded JVM), and evaluate them on an identical 500-host campus fabric using an RFC 8456-aligned methodology with ten runs per controller. All three provide near-zero loss, sub-millisecond jitter, and preserve established sessions, but their control-plane behavior differs sharply. OpenDaylight and ONOS keep reactive latency low and stable, whereas Ryu serializes reactive flow installation behind periodic rotation work, increasing reactive RTT by about 100x and causing a small number of setup-time failures. Ryu uses far less memory, while ONOS achieves OpenDaylight-class reactive latency with the lowest CPU utilization of the three and a smaller live heap than OpenDaylight. These results expose distinct resource-versus-responsiveness operating points and show that controller selection should be treated as a first-class design decision in SDN-based MTD.

Explore related subjects

cs.NI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Souhail Chakkour, Umesh Biswas, Charan Gudla. 2026-09-28. Resource versus Responsiveness: Benchmarking SDN Controller Runtimes for a Moving-Target-Defense Control Plane at Scale. https://arxiv.org/abs/2609.35585

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Cell-Free Massive MIMO Under Mobility: A Fairness-Differentiated Handover Scheme

While cell-free massive MIMO (CF-mMIMO) offers high and uniform network-wide throughput in static networks, its performance in mobile networks is not yet fully addressed. In this paper, we evaluate the throughput performance of urban mobile CF-mMIMO networks under a comprehensive throughput model and show that it suffers from large performance degradation due to the combined effect of channel aging and handover overheads. To restore the uniformly good performance of CF-mMIMO under mobility, we formulate a novel optimization problem to maximize the nett throughput that considers both channel aging and handover cost. We derive a near-optimal solution nearOpt for our transformed and relaxed optimization problem with Newton's method. We then design a heuristic handover algorithm, FairDiff, to differentiate prioritized and optional handovers using a policy threshold based on Jain's fairness index, in order to achieve uniform throughput over the network. Our extensive evaluation of the mobile throughput performance of our handover schemes in realistic urban mobile networks shows that, unlike the existing literature benchmarks that obtain very low throughput under mobility, our FairDiff scheme consistently achieves the near-optimal throughput comparable to nearOpt and highest network-wide throughput with the lowest computational complexity among all considered schemes. We thus for the first time propose a handover scheme that delivers the promise of uniformly good throughput for mobile CF-mMIMO, making it a feasible architecture for practical mobile networks.

cs.NI↗
arXiv2026

Embodied AI in 6G Networks: From Intelligent Connectivity to Physical Intelligence

Embodied artificial intelligence (AI) couples perception and learned decision making to actions that change the physical world. This coupling distinguishes an embodied agent from a conventional connected controller: the agent maintains task state and uncertainty, reasons about the consequences of actions, and adapts from subsequent observations. Wireless networking becomes relevant when perception, inference, or coordination is distributed, but it should not replace local safety control. This article develops a tutorial perception--communication--action (PCA) architecture that exposes task state, action deadlines, uncertainty, agent intent, and safety envelopes to a 6G orchestration plane. It separates capabilities already addressed by 5G and 5G-Advanced from functions that motivate 6G, including task-state interfaces, semantic freshness, predictive digital twins, and safety-aware coordination across agents. A multi-robot simulation study is retained to illustrate joint sensing, communication, and computation control. The results show where network orchestration improves task utility and where local autonomy remains essential.

cs.NI↗
arXiv2026

HOCCL: Offloading Collective Communication from GPU Cores to Accelerate Distributed Training

Large language model training involves massive computation on GPU streaming multiprocessors (SMs), the primary compute units of GPUs. Since SMs host specialized accelerators such as Tensor Cores, their efficient utilization is critical to training efficiency. Unfortunately, existing collective communication systems compete with computation for SMs, as they consume SMs for communication-related data movement and synchronization operations. We observe that communication can, in principle, be driven by DMA engines, thereby eliminating SM involvement in communication. Based on this insight, we propose HOCCL, a zero-SM collective communication framework consisting of three components: a stream manager, a point-to-point (P2P) executor, and a collective scheduler. The stream manager preserves operator-level temporal ordering with other GPU kernels. The P2P executor enables zero-SM point-to-point communication, while the collective scheduler orchestrates P2P transfers to maximize bandwidth. Experiments show that HOCCL preserves near-peak communication performance, achieving within 3% of the state of the art on average, while eliminating communication occupancy on nearly 10% of total GPU SMs. By freeing SM resources for computation, HOCCL improves end-to-end training throughput by up to 5%.

cs.NI↗