ESP: A Machine Learning Approach to Predicting Application Interference

Independent applications co-scheduled on the same hardware will interfere with one another, affecting performance in complicated ways. Predicting this interference is key to efficiently scheduling applications on shared hardware, but forming accurate predictions is difficult because there are many shared hardware features that could lead to the interference. In this paper we investigate machine learning approaches (specifically, regularization) to understand the relation between those hardware features and application interference. We propose ESP, a highly accurate and fast regularization technique for application interference prediction. To demonstrate this practicality, we implement ESP and integrate it into a scheduler for both single and multi-node Linux/x86 systems and compare the scheduling performance to state-of-the-art heuristics. We find that ESP-based schedulers increase throughput by 1.25-1.8× depending on the scheduling scenario. Additionally, we find that ESP's accurate predictions allow schedulers to avoid catastrophic decisions, which heuristic approaches fundamentally cannot detect.