Network Scheduler Based on Reinforcement Learning for Minimizing the Maximum End-to-End Latency 


Vol. 47,  No. 9, pp. 1330-1340, Sep.  2022
10.7840/kics.2022.47.9.1330


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  Abstract

In this paper, a reinforcement learning(RL)-based scheduler to minimize the maximum network end-to-end latency is implemented in a single agent environment and a multi-agent environment. The RL model used the double deep Q-network (DDQN) with the prioritized experience replay (PER). Since the agents are unable to identify end-to-end latencies in the multi-agent environment, the state and reward were obtained using the estimated end-to-end latencies. Four network topologies were implemented and simulated to compare the reinforcement learning-based scheduler, FIFO, round robin (RR), and a simple heuristic algorithm (HA). As a result of simulation in fixed packet generation scenarios, the RL-based scheduler achieved the minimization of maximum end-to-end latency in all the topologies. The FIFO and RR schedulers could not minimize the maximum end-to-end latency in any of the topology, and the HA could not minimize the maximum end-to-end latency in a single topology. In scenarios with random flows generation, the RL-based scheduler performed better than the FIFO and RR, but performed the same as or worse than the HA, depending on the topology.

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[IEEE Style]

J. Kwon, J. Ryu, J. Joung, "Network Scheduler Based on Reinforcement Learning for Minimizing the Maximum End-to-End Latency," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 9, pp. 1330-1340, 2022. DOI: 10.7840/kics.2022.47.9.1330.

[ACM Style]

Juhyeok Kwon, Jihye Ryu, and Jinoo Joung. 2022. Network Scheduler Based on Reinforcement Learning for Minimizing the Maximum End-to-End Latency. The Journal of Korean Institute of Communications and Information Sciences, 47, 9, (2022), 1330-1340. DOI: 10.7840/kics.2022.47.9.1330.

[KICS Style]

Juhyeok Kwon, Jihye Ryu, Jinoo Joung, "Network Scheduler Based on Reinforcement Learning for Minimizing the Maximum End-to-End Latency," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 9, pp. 1330-1340, 9. 2022. (https://doi.org/10.7840/kics.2022.47.9.1330)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880