Chameleon

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-thefly 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.

Search for the Memory Duplicities in the Java Applications Using Shallow and Deep Object Comparison

“…One important inspiration for our work is [11], where both static and dynamic analysis is proposed as a tool for evaluating the usage of Java collections (or other structures that allows manipulation with a large number of elements). Another approach for the dynamic analysis of the collections efficiency is presented in the [12] -the technique proposed here aims not only to use collections more efficiently, but also to choose the most suitable collection for the application, based on the runtime analysis.…”