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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. 

Service provisioning can be enhanced with spectrally spatially flexible optical networks (SS-FONs) with multicore fibers; however, intercore crosstalk (XT) is a dominant impairment that complicates the problem of maintaining the quality of transmission (QoT) and resource allocation. The selection of modulation formats (MFs), due to their unique XT sensitivities, further increases the complexity. The routing, modulation, core, and spectrum assignment (RMCSA) problem must select the resources carefully to exploit the available capacity while meeting the desired QoT. In this paper, we propose an RMCSA algorithm called the tridental resource assignment (TRA) algorithm for transparent SS-FONs, and its variant, translucency-aware TRA (TaTRA), for translucent SS-FONs. TRA balances three different factors that affect network performance under dynamic resource allocation. We consider translucent networks with flexible regeneration and with and without modulation and spectrum conversion. Our resource assignment approach includes both an offline network planning component to calculate path priorities and an online/dynamic provisioning component to allocate resources. Extensive simulation experiments performed in realistic network scenarios indicate that TRA and TaTRA significantly reduce the bandwidth blocking probability by several orders of magnitude in some cases.