An Adaptive Tenuring Policy for Generation Scavengers

Abstract

One of the more promising automatic storage reclamation techniques, generation scavenging, suffers poor performance if many objects live for a fairly long time and then die. We have investigated the severity of this problem by simulating a two-generation scavenger using traces taken from actual 4-h sessions. There was a wide variation in the sample runs, with garbage-collection overhead ranging from insignificant, during three of the runs, to severe, during a single run. All runs demonstrated that performance could be improved with two techniques: segregating large bitmaps and strings, and adapting the scavenger's tenuring policy according to demographic feedback. We therefore incorporated these ideas into a commercial Smalltalk implementation. These two improvements deserve consideration for any storage reclamation strategy that utilizes a generation scavenger.