Abstract

This paper proposes a data-driven bandwidth allocation (BA) framework for periodically and dynamically reconfiguring an elastic optical network according to predictive BA (PBA) models. The proposed framework is scalable to the number of network connections and also adaptive to the increasing traffic of each network connection separately and to the overall network load as well. This is achieved by formulating the BA problem as a partially observable Markov decision process (POMDP), which constitutes a reinforcement learning (RL) algorithm. Specifically, RL is performed continuously and independently (locally) for each network connection according to the most recent data that describe the traffic demand behavior of each network connection. A central controller monitors the network performance that is jointly achieved from all the PBA models and is capable of dynamically modifying the reward function of the POMDP, ensuring that the quality-of-service (QoS) requirements are met. A reward function $R(C)$ is examined with a clear impact on the network performance when $C$ is modified. For evaluating the network performance, for each $R(C)$ , the routing and spectrum allocation (RSA) problem is solved according to an integer linear programming (ILP) algorithm and an RSA heuristic alternative, with both the ILP and the heuristic RSA taking as inputs the outputs of the inferred PBA models. Results indicate that, with the appropriate settings of $C$ , bandwidth is efficiently allocated, while ensuring that the QoS requirements are met.

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