Multiple applications executing concurrently on a multicore system interfere with each other at different shared resources such as main memory and shared caches. Such inter-application interference, if uncontrolled, results in high system performance degradation and unpredictable application slowdowns. While previous work has proposed application-aware memory scheduling as a solution to mitigate inter-application interference and improve system performance, previously proposed memory scheduling techniques incur high hardware complexity and unfairly slowdown some applications. Furthermore, previously proposed memory-interference mitigation techniques are not designed to precisely control application performance. This dissertation seeks to achieve high and controllable performance in multicore systems by mitigating and quantifying the impact of shared resource interference. First, towards mitigating memory interference and achieving high performance, we propose the Blacklisting memory scheduler that achieves high performance and fairness at low complexity. Next, towards quantifying the impact of memory interference and achieving controllable performance in the presence of memory bandwidth interference, we propose the Memory Interference induced Slowdown Estimation (MISE) model. We propose and demonstrate two use cases that can leverage MISE to provide soft performance guarantees and high overall performance/fairness. Finally, we seek to quantify the impact of shared cache interference on application slowdowns, in addition to memory bandwidth interference. Towards this end, we propose the Application Slowdown Model (ASM). We propose and demonstrate several use cases of ASM that leverage it to provide soft performance guarantees and improve performance and fairness.