  Subspace identification (SID) has been widely used in system identification
and control fields since it can estimate system models only relying on the
input and output data by reliable numerical operations such as singular value
decomposition (SVD). However, high-dimension Hankel matrices are involved to
store these data and used to obtain the system models, which increases the
computation amount of SID and leads SID not suitable for the large-scale or
real-time identification tasks. In this paper, a novel fast SID method based on
cloud workflow processing and container technology is proposed to accelerate
the traditional algorithm. First, a workflow-based structure of SID is designed
to match the distributed cloud environment, based on the computational feature
of each calculation stage. Second, a containerised cloud workflow processing
system is established to execute the logic- and data- dependent SID workflow
mission based on Kubernetes system. Finally, the experiments show that the
computation time is reduced by at most $91.6\%$ for large-scale SID mission and
decreased to within 20 ms for the real-time mission parameter.
