Dynamic trace-based sampling algorithm for memory usage tracking of enterprise applications

Abstract

Excessive memory usage in software applications has become a frequent issue. A high degree of parallelism and the monitoring difficulty for the developer can quickly lead to memory shortage, or can increase the duration of garbage collection cycles. There are several solutions introduced to monitor memory usage in software. However they are neither efficient nor scalable. In this paper, we propose a dynamic tracing-based sampling algorithm to collect and analyse run time information and metrics for memory usage. It is implemented as a kernel module which gathers memory usage data from operating system structures only when a predefined condition is set or a threshold is passed. The thresholds and conditions are preset but can be changed dynamically, based on the application behavior. We tested our solutions to monitor several applications and our evaluation results show that the proposed method generates compact trace data and reduces the time needed for the analysis, without loosing precision.