  Multi-tenancy for latency-critical applications leads to re-source
interference and unpredictable performance. Core reconfiguration opens up more
opportunities for colocation,as it allows the hardware to adjust to the dynamic
performance and power needs of a specific mix of co-scheduled applications.
However, reconfigurability also introduces challenges, as even for a small
number of reconfigurable cores, exploring the design space becomes more time-
and resource-demanding.
  We present CuttleSys, a runtime for reconfigurable multi-cores that leverages
scalable and lightweight data mining to quickly identify suitable core and
cache configurations for a set of co-scheduled applications. The runtime
combines collaborative filtering to infer the behavior of each job on every
core and cache configuration, with Dynamically Dimensioned Search to
efficiently explore the configuration space. We evaluate CuttleSys on
multicores with tens of reconfigurable cores and show up to 2.46x and 1.55x
performance improvements compared to core-level gating and oracle-like
asymmetric multicores respectively, under stringent power constraints.
