Moving garbage collectors (GCs) typically free memory by evacuating live objects in order to reclaim contiguous memory regions. Evacuation is typically done either during tracing (scavenging), or after tracing when identification of live objects is complete (mark–evacuate). Scavenging typically requires more memory (memory for all objects to be moved), but performs less work in sparse memory areas (single pass). This makes it attractive for collecting young objects. Mark–evacuate typically requires less memory and performs less work in memory areas with dense object clusters, by focusing relocation around sparse regions, making it attractive for collecting old objects. Mark–evacuate also completes identification of live objects faster, making it attractive for concurrent GCs that can reclaim memory immediately after identification of live objects finishes (as opposed to when evacuation finishes), at the expense of more work compared to scavenging, for young objects.

We propose an alternative approach for concurrent GCs to combine the benefits of scavenging with the benefits of mark–evacuate, for young objects. The approach is based on the observation that by the time young objects are relocated by a concurrent GC, they are likely to already be unreachable. By performing relocation lazily, most of the relocations in the defragmentation phase of mark–evacuate can typically be eliminated. Similar to scavenging, objects are relocated during tracing with the proposed approach. However, instead of relocating all objects that are live in the current GC cycle, it lazily relocates profitable sparse object clusters that survived from the previous GC cycle. This turns the memory headroom that concurrent GCs typically ``waste'' in order to safely avoid running out of memory before GC finishes, into an asset used to eliminate much of the relocation work, which constitutes a significant portion of the GC work.

We call this technique mark–scavenge and implement it on-top of ZGC in OpenJDK in a collector we call MS-ZGC. We perform a performance evaluation that compares MS-ZGC against ZGC. The most striking result is (up to) 91% reduction in relocation of dead objects (depending on machine-dependent factors).