  This paper summarizes the ideas and key concepts in MISE (Memory
Interference-induced Slowdown Estimation), which was published in HPCA 2013
[97], and examines the work's significance and future potential. Applications
running concurrently on a multicore system interfere with each other at the
main memory. This interference can slow down different applications
differently. Accurately estimating the slowdown of each application in such a
system can enable mechanisms that can enforce quality-of-service. While much
prior work has focused on mitigating the performance degradation due to
inter-application interference, there is little work on accurately estimating
slowdown of individual applications in a multi-programmed environment. Our goal
is to accurately estimate application slowdowns, towards providing predictable
performance.
  To this end, we first build a simple Memory Interference-induced Slowdown
Estimation (MISE) model, which accurately estimates slowdowns caused by memory
interference. We then leverage our MISE model to develop two new memory
scheduling schemes: 1) one that provides soft quality-of-service guarantees,
and 2) another that explicitly attempts to minimize maximum slowdown (i.e.,
unfairness) in the system. Evaluations show that our techniques perform
significantly better than state-of-the-art memory scheduling approaches to
address the same problems.
  Our proposed model and techniques have enabled significant research in the
development of accurate performance models [35, 59, 98, 110] and interference
management mechanisms [66, 99, 100, 108, 119, 120].
