Chameleon: Adaptive selection of collections

Abstract

Languages such as Java and C#, as well as scripting languages like Python, and Ruby, make extensive use of Collection classes. A collection implementation represents a fixed choice in the dimensions of operation time, space utilization, and synchronization. Using the collection in a manner not consistent with this fixed choice can cause significant performance degradation. In this paper, we present CHAMELEON, a low-overhead automatic tool that assists the programmer in choosing the appropriate collection implementation for her application. During program execution, CHAMELEON computes elaborate trace and heap-based metrics on collection behavior. These metrics are consumed on-the-fly by a rules engine which outputs a list of suggested collection adaptation strategies. The tool can apply these corrective strategies automatically or present them to the programmer. We have implemented CHAMELEON on top of a IBM's J9 production JVM, and evaluated it over a small set of benchmarks. We show that for some applications, using CHAMELEON leads to a significant improvement of the memory footprint of the application. Copyright © 2009 ACM.