Memory Pressure: Early Identification and Proactive Taming in Resource Constrained Devices

Abstract

Latency critical systems face significant performance challenges when encountering memory pressure, and the existing approaches mainly focus on reactive and instantaneous approaches, but they often fail to accurately identify the root cause of memory pressure, resulting in delayed and ineffective response strategies. In this work, we address this limitation by proposing an alternative approach to proactively detect memory pressure and identify the root cause in such systems. Our method enables the activation and deactivation of extended process-level profiling based on the predicted memory pressure, facilitating the identification of the root cause process. Through evaluation, we achieved an 85% accuracy in forecasting memory pressure situations and correctly identified the responsible process in 83% of use-cases. We also propose an effective adaptive sampling framework to further optimize the system monitoring and data collection, and was able to leverage a state of the art technique to make useful sampling rate changes 78.5% of the time.