A generic persistent object store
Software Engineering Journal - Object-oriented systems
Safe and efficient sharing of persistent objects in Thor
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Semi-automatic, self-adaptive control of garbage collection rates in object databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Partitioned garbage collection of a large object store
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Garbage collection for a client-server persistent object store
ACM Transactions on Computer Systems (TOCS)
Managing Reentrant Structures Using Reference Counts
ACM Transactions on Programming Languages and Systems (TOPLAS)
A Highly Effective Partition Selection Policy for Object Database Garbage Collection
IEEE Transactions on Knowledge and Data Engineering
Incremental Collection of Mature Objects
IWMM '92 Proceedings of the International Workshop on Memory Management
Incremental Garbage Collection of a Persistent Object Store using PMOS
Proceedings of the 8th International Workshop on Persistent Object Systems (POS8) and Proceedings of the 3rd International Workshop on Persistence and Java (PJW3): Advances in Persistent Object Systems
Evaluating Partition Selection Policies Using the PMOS Garbage Collector
POS-9 Revised Papers from the 9th International Workshop on Persistent Object Systems
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There are only a few garbage collection algorithms that have been designed to operate over massive object stores. These algorithms operate at two levels, locally via incremental collection of small partitions and globally via detection of cross partition garbage, including cyclic garbage. At each level there is a choice of collection mechanism. For example, the PMOS collector employs tracing at the local level and reference counting at the global level. Another approach implemented in the Thor object database uses tracing at both levels. In this paper we present two new algorithms that both employ reference counting at the local level. One algorithm uses reference counting at the higher level and the other uses tracing at the higher level. An evaluation strategy is presented to support comparisons between these four algorithms and preliminary experiments are outlined.