  Co-locating latency-critical services (LCSs) and best-effort jobs (BEJs)
constitute the principal approach for enhancing resource utilization in
production. Nevertheless, the co-location practice hurts the performance of
LCSs due to resource competition, even when employing isolation technology.
Through an extensive analysis of voluminous real trace data derived from two
production clusters, we observe that BEJs typically exhibit periodic execution
patterns and serve as the primary sources of interference to LCSs. Furthermore,
despite occupying the same level of resource consumption, the diverse
compositions of BEJs can result in varying degrees of interference on LCSs.
Subsequently, we propose PISM, a proactive Performance Interference Scoring and
Mitigating framework for LCSs through the optimization of BEJ scheduling.
Firstly, PISM adopts a data-driven approach to establish a characterization and
classification methodology for BEJs. Secondly, PISM models the relationship
between the composition of BEJs on servers and the response time (RT) of LCSs.
Thirdly, PISM establishes an interference scoring mechanism in terms of RT,
which serves as the foundation for BEJ scheduling. We assess the effectiveness
of PISM on a small-scale cluster and through extensive data-driven simulations.
The experiment results demonstrate that PISM can reduce cluster interference by
up to 41.5%, and improve the throughput of long-tail LCSs by 76.4%.
