Spatial Encoding of Flow Spaces for Intelligent SDN Applications

2025-09-19

AI Summary

In intelligent software-defined networking (SDN), conventional flow-space encoding methods struggle to simultaneously achieve computational efficiency and spatial locality, thereby limiting the decision-making performance of reinforcement learning (RL). To address this, we propose a spatially aware Bloom filter encoding scheme—the first to leverage locality-preserving Bloom filters for IP flow-pair encoding—explicitly modeling geographic locality to enhance the relevance and predictability of RL state representations. We integrate this encoding into a Deep Q-Network (DQN) to design a low-latency reactive forwarding policy with adaptive flow eviction. Evaluated on 10 hours of real-world IoT traffic, our approach reduces normalized miss rates by up to 7% and 8% compared to LRU and LFU, respectively, significantly improving forwarding efficiency. This work establishes a novel paradigm for RL-driven SDN applications by bridging spatial semantics with scalable flow encoding.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Machine Learning: Reinforcement Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements

Abstract

Efficient encoding of network flow spaces while preserving spatial locality is essential for intelligent Software-Defined Networking (SDN) applications, particularly those employing reinforcement learning (RL) methods in a reactive manner. In this work, we introduce a spatially aware Bloom Filter-based approach to encode IP flow pairs, leveraging their inherent geographical locality. Through controlled experiments using IoT traffic data, we demonstrate that Bloom Filters effectively preserve spatial relationships among flows. Our findings show that Bloom Filters degrade gracefully, maintaining predictable spatial correlations critical for RL state representation. We integrate this encoding into a DQN-based eviction strategy for reactive SDN forwarding. Experiments show that Bloom Filter-encoded, spatially aware flow representation enables up to 7% and 8% reduction in normalized miss rate over LRU and LFU, respectively, across 10 hours of traffic, demonstrating potential for low-latency applications. This experiment justifies the usefulness of preserving spatial correlation by encoding the flow space into a manageable size, opening a novel research direction for RL-based SDN applications.

Problem

• Efficiently encoding network flow spaces while preserving spatial locality
• Developing intelligent SDN applications using reinforcement learning methods
• Creating manageable flow representations for low-latency networking applications

Innovation

• Spatially aware Bloom Filter encoding for IP flow pairs
• Bloom Filters preserve spatial relationships among network flows
• DQN-based eviction strategy with Bloom Filter-encoded flow representation