BlueJay: A Platform to Quantifying the Impact of Memory Latency on Datacenter Application Performance

Understanding the impact of memory latency on datacenter application performance can provide decision support to memory subsystem designers. Currently, various methods are available to quantify this impact, including cycle-accurate simulators, memory-level parallelism models, and software delay injection techniques. However, these methods suffer from several limitations, such as slow simulation speed, inaccuracy, and insufficient compatibility that requires application modification.This paper proposes BlueJay, a novel platform to quantify the impact of memory latency on the end-to-end performance of datacenter applications, avoiding slow simulation and providing high accuracy and compatibility. The key idea of BlueJay is to control the consumed memory bandwidth and read/write ratio, thereby manipulating memory latency to achieve quantification. Experiment shows that BlueJay provides accurate quantification with an average error of 3.04%. In addition, we built regression models for five applications deployed at scale in Alibaba data centers. The results reveal that a 10 ns increase in memory latency results in a performance decrease of 2.61%-3.31% for enterprise Java applications and databases, while the elastic block storage service experiences a more modest performance decrease of 0.73%-0.92%.