Aggressive prefetching improves system performance by hiding and tolerating off-chip memory latency. However, on a multicore system, prefetchers of different cores contend for shared resources and aggressive prefetching can degrade the overall system performance. The role of a prefetcher aggressiveness engine is to select appropriate aggressiveness levels for each prefetcher such that shared resource contention caused by prefetchers is reduced, thereby improving system performance. State-of-the-art prefetcher aggressiveness engines monitor metrics such as prefetch accuracy, bandwidth consumption, and last-level cache pollution. They use carefully tuned thresholds for these metrics, and when the thresholds are crossed, they trigger aggressiveness control measures. These engines have three major shortcomings: (1) thresholds are dependent on the system configuration (cache size, DRAM scheduling policy, and cache replacement policy) and have to be tuned appropriately, (2) there is no single threshold that works well across all the workloads, and (3) thresholds are oblivious to the phase change of applications. To overcome these shortcomings, we propose CAFFEINE, a model-based approach that analyzes the effectiveness of a prefetcher and uses a metric called net utility to control the aggressiveness. Our metric provides net processor cycles saved because of prefetching by approximating the cycles saved across the memory subsystem, from last-level cache to DRAM. We evaluate CAFFEINE across a wide range of workloads and compare it with the state-of-the-art prefetcher aggressiveness engine. Experimental results demonstrate that, on average (geomean), CAFFEINE achieves 9.5% (as much as 38.29%) and 11% (as much as 20.7%) better performance than the best-performing aggressiveness engine for four-core and eight-core systems, respectively. © 2015 ACM.