Scalable Runtime Bloat Detection Using Abstract Dynamic Slicing

Abstract

Many large-scale Java applications suffer from runtime bloat. They execute large volumes of methods and create many temporary objects, all to execute relatively simple operations. There are large opportunities for performance optimizations in these applications, but most are being missed by existing optimization and tooling technology. While JIT optimizations struggle for a few percent improvement, performance experts analyze deployed applications and regularly find gains of 2× or more. Finding such big gains is difficult, for both humans and compilers, because of the diffuse nature of runtime bloat. Time is spread thinly across calling contexts, making it difficult to judge how to improve performance. Our experience shows that, in order to identify large performance bottlenecks in a program, it is more important to understand its dynamic dataflow than traditional performance metrics, such as running time.This article presents a general framework for designing and implementing scalable analysis algorithms to find causes of bloat in Java programs. At the heart of this framework is a generalized form of runtime dependence graph computed by abstract dynamic slicing, a semantics-aware technique that achieves high scalability by performing dynamic slicing over bounded abstract domains. The framework is instantiated to create two independent dynamic analyses, copy profiling and cost-benefit analysis, that help programmers identify performance bottlenecks by identifying, respectively, high-volume copy activities and data structures that have high construction cost but low benefit for the forward execution.We have successfully applied these analyses to large-scale and long-running Java applications. We show that both analyses are effective at detecting inefficient operations that can be optimized for better performance. We also demonstrate that the general framework is flexible enough to be instantiated for dynamic analyses in a variety of application domains.

Prioritising test scripts for the testing of memory bloat in web applications

“…By removing these inefficiently used containers or replacing them with local data structures, the memory performance of Java applications can be optimised. They also proposed methods using dynamic slicing, copy profiling etc., to help diagnose the causes of bloat [8]. Mitchell et al [2] presented a method for diagnosing memory bloat in Java applications based on health signatures.…”

Memory Cities: Visualizing Heap Memory Evolution Using the Software City Metaphor

“…These "expert mode features" should not be enabled by default but could be switched on by experienced memory analysts. Memory cites can also be expanded to support other typical memory analysis tasks such as memory churn analysis [70], [71] or memory bloat analysis [72]- [76].…”

Evaluating an Interactive Memory Analysis Tool: Findings from a Cognitive Walkthrough and a User Study

“…It is often caused by heavily using (object-oriented) abstractions, such as in over-generalized data structures. Most techniques for detecting memory bloat thus focus on analyzing data structures requiring many auxiliary objects [77] or inefficient usage of data structures operations for adding, getting, or removing elements [124,127].…”