  Optimizing data transfers is critical for improving job performance in
data-parallel frameworks. In the hybrid data center with both wired and
wireless links, reconfigurable wireless links can provide additional bandwidth
to speed up job execution. However, it requires the scheduler and transceivers
to make joint decisions under coupled constraints. In this work, we identify
that the joint job scheduling and bandwidth augmentation problem is a complex
mixed integer nonlinear problem, which is not solvable by existing optimization
methods. To address this bottleneck, we transform it into an equivalent problem
based on the coupling of its heuristic bounds, the revised data transfer
representation and non-linear constraints decoupling and reformulation, such
that the optimal solution can be efficiently acquired by the Branch and Bound
method. Based on the proposed method, the performance of job scheduling with
and without bandwidth augmentation is studied. Experiments show that the
performance gain depends on multiple factors, especially the data size.
Compared with existing solutions, our method can averagely reduce the job
completion time by up to 10% under the setting of production scenario.
