tenuring policies for generation-based storage reclamation
One of the most promising automatic storage reclamation techniques, generation-based storage reclamation, 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 Generation Scavenging automatic storage reclamation from traces of actual four-hour sessions. There was a wide variation in the sample runs, with garbage-collection overhead ranging from insignificant, during the interactive runs, to severe, during a single non-interactive run. All runs demonstrated that performance could be improved with two techniques: segregating large bitmaps and strings, and mediating tenuring with demographic feedback. These two improvements deserve consideration for any generation-based storage reclamation strategy.
policy generation storage performance object live timing traces variation sampling interaction strings feedback strategies collect