Experience-driven Networking: A Deep Reinforcement Learning based Approach (1801.05757v1)

Abstract: Modern communication networks have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this paper, we develop a novel experience-driven approach that can learn to well control a communication network from its own experience rather than an accurate mathematical model, just as a human learns a new skill (such as driving, swimming, etc). Specifically, we, for the first time, propose to leverage emerging Deep Reinforcement Learning (DRL) for enabling model-free control in communication networks; and present a novel and highly effective DRL-based control framework, DRL-TE, for a fundamental networking problem: Traffic Engineering (TE). The proposed framework maximizes a widely-used utility function by jointly learning network environment and its dynamics, and making decisions under the guidance of powerful Deep Neural Networks (DNNs). We propose two new techniques, TE-aware exploration and actor-critic-based prioritized experience replay, to optimize the general DRL framework particularly for TE. To validate and evaluate the proposed framework, we implemented it in ns-3, and tested it comprehensively with both representative and randomly generated network topologies. Extensive packet-level simulation results show that 1) compared to several widely-used baseline methods, DRL-TE significantly reduces end-to-end delay and consistently improves the network utility, while offering better or comparable throughput; 2) DRL-TE is robust to network changes; and 3) DRL-TE consistently outperforms a state-ofthe-art DRL method (for continuous control), Deep Deterministic Policy Gradient (DDPG), which, however, does not offer satisfying performance.

• The paper demonstrates that DRL can be optimized for network control using TE-aware exploration and actor-critic-based prioritized experience replay.
• The paper shows significant performance gains, reducing end-to-end delays by 44.2%-70.5% and boosting network utility by up to 26.4% compared to traditional methods.
• The paper validates DRL-TE with ns-3 simulations across various network topologies, highlighting its adaptability to dynamic network conditions.

Insightful Overview of "Experience-driven Networking: A Deep Reinforcement Learning based Approach"

The paper "Experience-driven Networking: A Deep Reinforcement Learning based Approach" presents a significant contribution to traffic engineering (TE) by employing a novel Deep Reinforcement Learning (DRL)-based framework called DRL-TE. The authors explore the potential of DRL to perform model-free control in communication networks—and thereby offer a solution to the complex networking environment that traditional models struggle to accurately capture.

Core Contributions

The primary accomplishments of the paper lie in leveraging DRL for model-free network control and the introduction of specialized techniques to optimize DRL for TE, namely, TE-aware exploration and actor-critic-based prioritized experience replay. These dedicated methods showcase how DRL can be tailored to address the intricacies of network dynamics and decision-making processes.

1. TE-aware Exploration: The authors propose a new exploration strategy that ties DRL exploration with a reliable baseline TE solution, enhancing the agent's ability to navigate high-dimensional and continuous action spaces effectively.
2. Actor-Critic-Based Prioritized Experience Replay: This method extends the experience replay technique by assigning sampling priorities based on both the Temporal-Difference (TD) error and the $Q$-gradient, thereby allowing more impactful transition samples to guide the learning process of both actor and critic networks.

Methodology and Evaluation

The framework, DRL-TE, is comprehensively validated using ns-3, performing simulations on various network topologies, such as NSFNET, ARPANET, and a randomly generated topology. Through extensive experiments with both packet-level simulations and compared against benchmark methods like Shortest Path (SP), Load Balancing (LB), Network Utility Maximization (NUM), and an existing DRL technique (DDPG), the DRL-TE framework consistently reduces end-to-end delays while maintaining or improving throughput and utility scores.

1. Strong Numerical Results: The results highlight a significant reduction in end-to-end delay—averaging 44.2% to 70.5%—and improvements in network utility by up to 26.4% over existing methods. The DRL-TE method proves robust across different traffic loads and network structures.
2. Demonstration of DRL's Utility over Traditional Methods: By outperforming competing architectures like DDPG, which showed limitations in this domain, DRL-TE emphasizes the importance of specialized optimization for DRL models to address specific networking challenges.

Theoretical and Practical Implications

The theoretical contributions of the paper extend our understanding of DRL's applicability in networking, demonstrating how traditional model-free approaches can be augmented for complex resource allocation scenarios. Practically, DRL-TE illustrates that DRL-based traffic engineering can dynamically learn and adapt to network uncertainties, making it a valuable tool for future networking technologies like SDNs and other complex environments.

Future Prospects

As DRL continues to advance, the foundational insights from this framework may inspire further exploration into other resource allocation problems within communication networks. Subsequent investigations could enhance generalization across more diverse network conditions and incorporate resource adaptations for mobile or edge-computing environments—with DRL paving the way for more agile and resilient network control methods.

Overall, the paper enriches the applied understanding of DRL in network engineering and serves as a starting point for future explorations in experience-driven network control methodologies, setting a precedent for further research and practical deployment in increasingly dynamic and unpredictable network settings.