Analyzing the Evolution of Data Structures in Trace-Based Memory Monitoring

Abstract

Modern software systems are becoming increasingly complex and are thus more prone to performance degradation due to memory leaks. Memory leaks occur if objects that are not needed anymore are still unintentionally kept alive. While there exists a variety of state-of-the-art memory monitoring tools, most of them only use memory snapshots, i.e., heap dumps, to analyze an application’s live objects at a single point in time. This does not allow developers to identify data structures that grow over time. Tracebased monitoring tools tackle this problem by recording memory events, e.g., allocations or object moves performed by the garbage collector (GC), throughout an application’s run time. In this paper, we present ongoing research on the use of memory traces for detecting the root causes of memory leaks introduced by growing data structures. This encompasses (1) a domain-specific language (DSL) to describe arbitrary data structures, (2) an algorithm to detect instances of previously defined data structures in reconstructed heaps, as well as (3) techniques to analyze the temporal evolution of these data structure instances to identify those possibly involved in memory leaks. All these concepts have been integrated into AntTracks, a trace-based memory monitoring tool, to prove their feasibility.