  Cloud data centers are evolving fast. At the same time, today's large-scale
data analytics applications require non-trivial performance tuning that is
often specific to the applications, workloads, and data center infrastructure.
We propose TeShu, which makes network shuffling an extensible unified service
layer common to all data analytics. Since an optimal shuffle depends on a
myriad of factors, TeShu introduces parameterized shuffle templates,
instantiated by accurate and efficient sampling that enables TeShu to
dynamically adapt to different application workloads and data center layouts.
Our preliminary experimental results show that TeShu efficiently enables
shuffling optimizations that improve performance and adapt to a variety of data
center network scenarios.
