Model-based prediction of automatic memory management and garbage collection behavior

Abstract

Performance models focus on resource consumption and the effects of CPU, network, or hard-disk utilization. These resources usually have the largest effect on the response times and throughput of an application. However, deficient memory management can have severe effects on an application and its runtime, such as overlong response times or even crashes. As memory management has been disregarded in performance simulations, we address this gap with an approach based on memory measurements and derived metrics to predict the behavior of this resource and the effects on the CPU. Although numerous works exist that analyze memory management and especially garbage collections, accurate prediction models are rare. We demonstrate the automatic extraction of memory behavior using a performance model generator. Furthermore, the approach is evaluated using the SPECjEnterprise2010 and the SPECjEnterpriseNEXT industry benchmark, using different resource environments, garbage collection algorithms, and workloads. This work demonstrates that a certain set of probabilities allows one to create a memory profile for an architecture and predict the behavior of the memory management. The results of such predictions can be used for better capacity planning (on-premise), cost-prediction (cloud), architecture evaluation and optimization, or memory profiling. This approach allows for a continuous model-based evaluation of an enterprise architecture regarding its memory footprint.