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The increasing demand for flexible and efficient optical networks has led to the development of Software-Defined Elastic Optical Networks (SD-EONs). These networks leverage the programmability of Software-Defined Networking (SDN) and the adaptability of Elastic Optical Networks (EONs) to optimize network performance under dynamic traffic conditions. However, existing simulation tools often fall short in terms of transparency, flexibility, and advanced functionality, limiting their utility in cutting-edge research. In this paper, we present a Flexible Unified Simulator for Intelligent Optical Networking (FUSION), a fully open-source simulator designed to address these limitations and provide a comprehensive platform for SD-EON research. FUSION integrates traditional routing and spectrum assignment algorithms with advanced machine learning and reinforcement learning techniques, including support for the Stable Baselines 3 library. The simulator also offers robust unit testing, a fully functional Graphical User Interface (GUI), and extensive documentation to ensure usability and reliability. Performance evaluations demonstrate the effectiveness of FUSION in modeling complex network scenarios, showcasing its potential as a powerful tool for advancing SD-EON research. 

Extreme-scale science applications are highly innovative and constantly evolving. They are expected to generate data in the petabyte and exabyte ranges. Erasure coding has been widely adopted for data storage in data center networks, where the data are encoded and stored in multiple locations. Therefore, an efficient data retrieval service is needed to transfer encoded data from selected multiple stored nodes to a single destination. Elastic optical networks are a promising backbone technology for data center communication due to their capability to efficiently and flexibly allocate the huge optical bandwidth to heterogeneous traffic demands. In this paper, the erasure-coded multi-sourced data retrieval routing and scheduling problem is studied for static traffic in elastic optical networks, and the objective is to minimize the total transmission completion time of all the requests. An integer linear programming formulation and low-complexity heuristic are proposed. Furthermore, analytical lower bounds are derived and a meta-heuristic, Tabu Search, is adopted to solve the problem. Numerical results are presented to show the effectiveness of the proposed methods.