We present the design and implementation of a new garbage collection framework that significantly generalizes existing copying collectors. The Beltway framework exploits and separates object age and incrementality. It groups objects in one or more increments on queues called belts, collects belts independently, and collects increments on a belt in first-in-first-out order. We show that Beltway configurations, selected by command line options, act and perform the same as semi-space, generational, and older-first collectors, and encompass all previous copying collectors of which we are aware.

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.

Over the years, research has been done on several techniques related to garbage collection. Many key insights for copying-based generational garbage collection tech-niques have been revealed. Yet, there is still room for improvement. In this thesis, we introduce various new techniques and algorithms to improve garbage collection. In particular, we introduce the bounded frame marking technique for tracking pointers. This technique allows for efficient computation of the root set. It reuses concepts from two existing techniques, card marking and remembered sets, and uses a bidirectional object layout to improve them by regulating space overhead and reducing the pointer scanning workload. We also present an algorithm to recursively mark reachable objects without using a stack (eliminating the usual space overhead). We adapt this algorithm to implement a depth-first copying collector and increase heap locality. We improve the older-first garbage collection algorithm and its generational variant by adding a mark phase that guarantees the collection of all garbage, including cyclic structures spanning many windows. Finally, we introduce a technique to deal with large objects. In order to test our ideas, we have designed and implemented a portable and extensible garbage collection framework within the SableVM open source Java virtual machine. In it, we have implemented semi-space, older-first, and generational copying garbage collection algorithms. Our experiments show that the bounded frame technique yields competitive performances on many benchmarks. They also show that, for most benchmarks, our depth-first traversal algorithm improves locality and thus increases performance. Our overall performance measurements show that, using our techniques, a garbage collector can deliver competitive performance and surpass existing collectors on various benchmarks.