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Experience-driven Networking: A Deep Reinforcement Learning based Approach
Zhiyuan Xu, Jian Tang, Jingsong Meng, Weiyi Zhang, Yanzhi Wang, Chi Harold Liu, Dejun Yang
TL;DR
Complex, dynamic communication networks are difficult to control with accurate mathematical models, especially for Traffic Engineering. The paper introduces DRL-TE, a model-free DRL framework with TE-specific exploration and prioritized replay, and evaluates it through packet-level simulations. DRL-TE reduces delay, improves utility, maintains better or comparable throughput, remains robust to network changes, and outperforms DDPG.
Problem
Complex, highly dynamic communication networks are difficult to model, predict, and control, while TE involves continuous actions that DQN-based DRL cannot directly handle.
Method
DRL-TE jointly learns network dynamics and control decisions with actor and critic DNNs, using TE-aware exploration and actor-critic-based prioritized experience replay.
Results
DRL-TE significantly reduces end-to-end delay versus SP, LB, NUM, and DDPG, with reductions of 51.6%, 28.6%, 74.6%, and 50.0% on the NSF topology under medium traffic load.
Takeaways & Limitations
DRL-TE consistently improves utility, offers better or comparable throughput, remains robust across traffic loads and topologies, and outperforms DDPG.
Abstract
from arXiv · showhide
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.
I. INTRODUCTION
Communication networks are difficult to model and control because they are complex and dynamic, motivating experience-driven, model-free control. The paper introduces DRL-TE for Traffic Engineering and evaluates it against established and DRL baselines.
- Complex, highly dynamic networks challenge traditional model-based resource allocation methods that assume accurate environment and demand models.
- Traffic Engineering seeks forwarding decisions for network flows that maximize a utility function, but common shortest-path and load-balancing approaches are not optimal.
- DRL offers model-free control suited to time-varying environments and sophisticated state spaces, while DQN-based DRL cannot directly handle TE’s continuous actions.
- The paper introduces DRL-TE, a DRL-based TE framework that jointly learns network dynamics and makes decisions using deep neural networks.
- TE-aware exploration and actor-critic-based prioritized experience replay optimize the general DRL framework specifically for TE.
- Packet-level ns-3 simulations on representative and random topologies show DRL-TE significantly outperforms widely used baseline methods.
II. DEEP REINFORCEMENT LEARNING (DRL)
This section presents the reinforcement-learning foundations used for network control, including value-based and actor-critic methods. It also explains why experience replay and target networks stabilize training and why continuous TE requires actor-critic methods.
- An RL agent observes states, selects actions, receives rewards, and learns a policy maximizing discounted cumulative reward.
- DQN uses a deep neural network to estimate state-action values and commonly selects actions with a greedy policy.
- Experience replay trains the network from randomly sampled past transitions, reducing correlation and helping avoid oscillations or divergence.
- A separate target network estimates training targets and updates its parameters slowly every C > 1 epochs to improve stability.
- DQN is unsuitable for TE because continuous action maximization requires difficult nonlinear optimization, while discretization can create too many actions.
- Actor-critic methods maintain parameterized actor and critic functions and provide a standard approach for continuous control.
III. PROBLEM STATEMENT
The paper formulates TE on a directed network with multiple sessions and candidate paths, allocating traffic across paths. Its objective balances throughput and delay through a general utility function.
- The network is modeled as a directed graph whose vertices are nodes, edges are links, and sessions connect source-destination pairs through candidate paths.
- TE selects rate allocations fk,j that determine how much traffic session k sends on each candidate path.
- Each path’s split ratio wk,j determines the probability that an arriving packet uses that path.
- The α-fairness utility increases with throughput, while α controls the tradeoff between fairness and efficiency; α = 1 yields proportional fairness.
- The proposed utility incorporates each session’s end-to-end throughput and delay, with σ expressing delay’s relative importance.
- The formulation targets a general communication network and can be extended to specific networks or scenarios by adding constraints.
IV. PROPOSED DRL-BASED CONTROL FRAMEWORK
DRL-TE frames traffic engineering as a continuous-control problem and combines TE-aware exploration with actor-critic-based prioritized replay. Its design specifies network states, TE actions, utility rewards, and stabilized actor-critic training.
- State, action, and reward: The state records each communication session’s throughput and delay, while the action specifies its traffic-splitting ratios across candidate paths.The reward is the total utility of all communication sessions.
- Framework design: DRL-TE treats traffic engineering as continuous control, using DDPG as its starting point because DQN does not support this setting.Direct DDPG application to TE does not achieve satisfying performance, motivating TE-specific improvements.
- TE-aware exploration: TE-aware exploration uses a base TE solution alongside actor output and randomized noise to balance exploration with exploitation.The base solution may be shortest-path routing, equal traffic distribution, or a NUM-based solution.
- Prioritized experience replay: Actor-critic prioritized replay ranks transitions using both TD error and the actor’s Q gradient, with a parameter controlling their relative importance.A small positive constant prevents zero-error transitions from being permanently excluded, and the prioritization exponent controls sampling concentration.
- Training procedure: The algorithm stores transitions, samples prioritized mini-batches with importance-sampling weights, updates actor and critic networks, and softly updates target networks.Target networks improve learning stability, while importance-sampling weights correct bias introduced by prioritized replay.
V. PERFORMANCE EVALUATION
The evaluation compares DRL-TE with three conventional TE methods and DDPG across representative and random topologies using packet-level ns-3 simulations. DRL-TE reduces delay, improves utility, maintains strong throughput, and remains robust across traffic loads and topologies.
- Evaluation setup: Packet-level ns-3 simulations compare DRL-TE with SP, LB, NUM, and DDPG across representative and randomly generated network topologies.The evaluation uses total end-to-end throughput, average packet delay, and total network utility as performance metrics.
- Delay: 51.6%, 28.6%, 74.6%, and 50.0%: DRL-TE reduces end-to-end delay versus SP, LB, NUM, and DDPG on the NSF topology at medium traffic load.The cited comparison uses a traffic demand window of [30]Mbps.
- Delay: 55.4%, 47.1%, 70.5%, and 44.2%: DRL-TE achieves average reductions in end-to-end delay across the evaluated comparisons.The passage reports these reductions while explaining that DRL-TE learns runtime dynamics and makes decisions with DNNs.
- Throughput: DRL-TE provides consistently higher throughput on NSF and throughput comparable to LB but higher than SP and NUM on ARPANET and random topologies.Throughput is not the sole objective of DRL-TE's reward function.
- Utility: 7.7%, 9.1%, 26.4%, and 12.6%: DRL-TE outperforms SP, LB, NUM, and DDPG in average total utility.The reported utility comparison spans the three evaluated topologies.
- Robustness and learning: DRL-TE remains consistently better across traffic-demand settings and topologies, while DDPG often remains at lower-reward local optima.DRL-TE reaches a good solution within a couple of thousand decision epochs; on the random topology, DDPG shows only minor early improvement.
VI. RELATED WORK
The related-work discussion distinguishes this paper from model-based networking and prior DRL research. It positions the work as the first application of DRL to communication-network resource allocation while adding TE-specific exploration and replay techniques.
- TE and NUM: Prior TE and NUM approaches commonly rely on queueing or optimization models, whereas this paper develops an experience- and data-driven model-free DRL approach.The paper also emphasizes explicit treatment of end-to-end delay and changing network conditions.
- Deep Reinforcement Learning: Earlier DRL successes focused largely on game-playing or other control settings, leaving communication-network resource allocation such as TE insufficiently investigated.The paper frames applying DRL to complicated networking control as an open question.
- Deep Reinforcement Learning: DDPG is a model-free actor-critic algorithm designed for continuous action spaces, providing the principal DRL comparison discussed in the paper.The paper contrasts its TE-oriented techniques with this general continuous-control method.
- Contribution: The paper identifies its work as the first effort applying DRL to communication-network resource allocation and introduces TE-specific exploration and prioritized experience replay.These techniques are presented as optimizations of the general DRL framework for TE.
VII. CONCLUSIONS
DRL-TE provides experience-driven, model-free traffic engineering by learning network dynamics and making decisions with actor and critic networks. Simulations show reduced delay, improved utility, robust performance under network changes, and better or comparable throughput than baseline methods.
- DRL-TE enables model-free traffic engineering by jointly learning network dynamics and making decisions through actor and critic DNNs.It is designed to control communication networks from experience rather than an accurate mathematical model.
- DRL-TE significantly reduces end-to-end delay and consistently improves total utility compared with several widely-used baseline methods.The framework offers better or comparable throughput in the reported simulations.
- DRL-TE remains robust to network changes across simulations on NSFNET, APRANET, and a random topology.The evaluation was implemented in ns-3 using a comprehensive simulation study.
- TE-aware exploration and actor-critic-based prioritized experience replay optimize the general DRL framework for traffic engineering.