Pinto-Rios, J., Calderon, F., Leiva, A., Hermosilla, G., Beghelli, A., Borquez-Paredes, D., et al. (2023). Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning Approach. Complexity, 2023, 4140594.
Abstract: A deep reinforcement learning (DRL) approach is applied, for the first time, to solve the routing, modulation, spectrum, and core allocation (RMSCA) problem in dynamic multicore fiber elastic optical networks (MCF-EONs). To do so, a new environment was designed and implemented to emulate the operation of MCF-EONs – taking into account the modulation format-dependent reach and intercore crosstalk (XT) – and four DRL agents were trained to solve the RMSCA problem. The blocking performance of the trained agents was compared through simulation to 3 baselines RMSCA heuristics. Results obtained for the NSFNet and COST239 network topologies under different traffic loads show that the best-performing agent achieves, on average, up to a four-times decrease in blocking probability with respect to the best-performing baseline heuristic method.
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Villalon, J., & Calvo, R. A. (2013). A Decoupled Architecture for Scalability in Text Mining Applications. J. Univers. Comput. Sci., 19(3), 406–427.
Abstract: Sophisticated Text Mining features such as visualization, summarization, and clustering are becoming increasingly common in software applications. In Text Mining, documents are processed using techniques from different areas which can be very expensive in computation cost. This poses a scalability challenge for real-life applications in which users behavior can not be entirely predicted. This paper proposes a decoupled architecture for document processing in Text Mining applications, that allows applications to be scalable for large corpora and real-time processing. It contributes a software architecture designed around these requirements and presents TML, a Text Mining Library that implements the architecture. An experimental evaluation on its scalability using a standard corpus is also presented, and empirical evidence on its performance as part of an automated feedback system for writing tasks used by real students.
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