Key Objects in Garbage Collection

When the cost of global garbage collection in a system grows large, the system can be redesigned to use generational collection. The newly-created objects usually have a much shorter half-life than average, and by concentrating the collector's efforts on them a large fraction of the garbage can be collected at a tiny fraction of the cost. The objects that survive generational collection may still become garbage, and the current practice is to perform occasional global garbage collections to purge these objects from the system, and again, the cost of doing these collections may become prohibitive when the volume of memory increases. Previous research has noted that the objects that survive generational collection often are born, promoted, and collected in large clusters. In this dissertation I show that carefully selected semantically or structurally important key objects can be drawn from the clusters and collected separately; when a key object becomes unreachable, the collector can take this as a hint to collect the cluster from which the key was drawn. To gauge the effectiveness of key objects, their use was simulated in ParcPlace's Object-works/Smalltalk system. The objects selected as keys were those that, as young objects, had pointers to them stored into old objects. The collector attempts to create a cluster for each key by gathering together all of the objects reachable from that key and from no previous key. Using this simple heuristic for key objects, the collector finds between 41% and 92% of the clustered garbage in a suite of simple test programs. Except for one program in the suite, about 95% of the time these key objects direct the collector to a cluster that is garbage. The exception should be heeded in improving the heuristics. In a replay of an interactive session, key object collection finds 59% of the clustered garbage and 66% of suggested targets are indeed garbage.