Generational collection has improved the efficiency of garbage collection in fast-allocating programs by focusing on collecting young garbage, but has done little to reduce the cost of collecting a heap containing large amounts of older data. A new generational technique, older-first collection, shows promise in its ability to manage older data.This paper reports on an implementation study that compared two older-first collectors to traditional (younger-first) generational collectors. One of the older-first collectors performed well and was often effective at reducing the first-order cost of collection relative to younger-first collectors. Older-first collectors perform especially well when objects have queue-like or random lifetimes.

If a fixed exponentially decreasing probability distribution function is used to model every object's lifetime, then the age of an object gives no information about its future life expectancy. This radioactive decay model implies there can be no rational basis for deciding which live objects should be promoted to another generation. Yet there remains a rational basis for deciding how many objects to promote, when to collect garbage, and which generations to collect.Analysis of the model leads to a new kind of generational garbage collector whose effectiveness does not depend upon heuristics that predict which objects will live longer than others.This result provides insight into the computational advantages of generational garbage collection, with implications for the management of objects whose life expectancies are difficult to predict.