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This paper summarizes the ideas and key concepts in MISE (MemoryInterference-induced Slowdown Estimation), which was published in HPCA 2013[97], and examines the work's significance and future potential. Applicationsrunning concurrently on a multicore system interfere with each other at themain memory. This interference can slow down different applicationsdifferently. Accurately estimating the slowdown of each application in such asystem can enable mechanisms that can enforce quality-of-service. While muchprior work has focused on mitigating the performance degradation due tointer-application interference, there is little work on accurately estimatingslowdown of individual applications in a multi-programmed environment. Our goalis to accurately estimate application slowdowns, towards providing predictableperformance.  To this end, we first build a simple Memory Interference-induced SlowdownEstimation (MISE) model, which accurately estimates slowdowns caused by memoryinterference. We then leverage our MISE model to develop two new memoryscheduling 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 performsignificantly better than state-of-the-art memory scheduling approaches toaddress the same problems.  Our proposed model and techniques have enabled significant research in thedevelopment of accurate performance models [35, 59, 98, 110] and interferencemanagement mechanisms [66, 99, 100, 108, 119, 120].

Predictable Performance and Fairness Through Accurate Slowdown Estimation in Shared Main Memory Systems