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A Temporal Deep Q Learning for Optimal Load Balancing in Software-Defined Networks.

Aakanksha Sharma¹, Venki Balasubramanian², Joarder Kamruzzaman²

¹Melbourne Institute of Technology (MIT), Melbourne, VIC 3000, Australia.

Sensors (Basel, Switzerland)

|February 24, 2024

Summary

This summary is machine-generated.

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Temporal Deep Q learning (tDQN) optimizes Software-Defined Networks (SDNs) for Internet of Things (IoT) traffic. This intelligent approach minimizes network latency and improves performance without adding controllers.

Area of Science:

Computer Science
Network Engineering
Artificial Intelligence

Background:

The Internet of Things (IoT) generates massive network traffic, overwhelming traditional networks.
Software-Defined Networks (SDNs) offer better traffic management but face control plane bottlenecks and load balancing issues in multi-controller setups.
Existing dynamic controller mapping solutions struggle with controller placement and real-time load balancing for fluctuating traffic.

Purpose of the Study:

To propose an intelligent solution for dynamic controller mapping in multi-controller SDNs to minimize network latency.
To address the challenges of unpredictable IoT traffic growth and heterogeneous device complexity.
To enhance the performance of dynamic SDN (dSDN) using a self-learning reinforcement-based model.

Main Methods:

Keywords:

SDN deep temporal reinforcement learning flow fluctuation latency minimization packet delivery ratio

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Implementation of Temporal Deep Q learning Network (tDQN) within the dSDN controller architecture.
Utilizing a reinforcement learning agent with a reward-punish scheme for optimizing switch-controller mapping decisions.
Formulating a multi-objective optimization problem to dynamically divert traffic based on flow fluctuations.

Main Results:

The tDQN approach effectively optimizes dynamic flow mapping and reduces network latency without increasing the number of controllers.
Extensive simulations demonstrate superior performance compared to traditional networks, eSDN, and dSDN.
Significant improvements observed in throughput, delay, jitter, packet delivery ratio, and packet loss across varied network scenarios.

Conclusions:

Temporal Deep Q learning provides an intelligent and effective solution for managing complex and dynamic SDN environments, particularly for IoT.
The tDQN model successfully balances load and minimizes latency, outperforming previous methods.
This research offers a scalable and efficient approach to future-proof SDN infrastructure against exponential traffic growth.