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Multiple applications executing concurrently on a multicore system interferewith each other at different shared resources such as main memory and sharedcaches. Such inter-application interference, if uncontrolled, results in highsystem performance degradation and unpredictable application slowdowns. Whileprevious work has proposed application-aware memory scheduling as a solution tomitigate inter-application interference and improve system performance,previously proposed memory scheduling techniques incur high hardware complexityand unfairly slowdown some applications. Furthermore, previously proposedmemory-interference mitigation techniques are not designed to precisely controlapplication performance.  This dissertation seeks to achieve high and controllable performance inmulticore systems by mitigating and quantifying the impact of shared resourceinterference. First, towards mitigating memory interference and achieving highperformance, we propose the Blacklisting memory scheduler that achieves highperformance and fairness at low complexity. Next, towards quantifying theimpact of memory interference and achieving controllable performance in thepresence of memory bandwidth interference, we propose the Memory Interferenceinduced Slowdown Estimation (MISE) model. We propose and demonstrate two usecases that can leverage MISE to provide soft performance guarantees and highoverall performance/fairness. Finally, we seek to quantify the impact of sharedcache interference on application slowdowns, in addition to memory bandwidthinterference. Towards this end, we propose the Application Slowdown Model(ASM). We propose and demonstrate several use cases of ASM that leverage it toprovide soft performance guarantees and improve performance and fairness.

Providing High and Controllable Performance in Multicore Systems Through Shared Resource Management