Lazy release consistency for software distributed shared memory
ISCA '92 Proceedings of the 19th annual international symposium on Computer architecture
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fine-grained sharing in a page server OODBMS
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Shasta: a low overhead, software-only approach for supporting fine-grain shared memory
Proceedings of the seventh international conference on Architectural support for programming languages and operating systems
Transactional client-server cache consistency: alternatives and performance
ACM Transactions on Database Systems (TODS)
A Survey of Recoverable Distributed Shared Virtual Memory Systems
IEEE Transactions on Parallel and Distributed Systems
The object data standard: ODMG 3.0
The object data standard: ODMG 3.0
An Asynchronous Avoidance-Based Cache Consistency Algorithm for Client Caching DBMSs
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
An Adaptive Hybrid Server Architecture for Client Caching ODBMSs
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Don't Be Lazy, Be Consistent: Postgres-R, A New Way to Implement Database Replication
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
TreadMarks: distributed shared memory on standard workstations and operating systems
WTEC'94 Proceedings of the USENIX Winter 1994 Technical Conference on USENIX Winter 1994 Technical Conference
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In this paper, we assess the performance of DSMIO cache-coherence algorithm implemented in a parallel object-based database management system (ODBMS). The distinguishing feature of DSMIO is its use of the lazy release memory consistency model and multiple writer protocol to reduce both the number and size of coherence messages required to keep coherent a distributed ODBMS across a cluster of PC servers. Using a large distributed database and several application workloads we evaluate DSMIO performance and also compare it against that of the well-known Call-Back Locking (CBL) algorithm. Our results showt hat both algorithms perform very well for read operations whereas DSMIO outperforms significantly CBL for write operations with DSMIO speed-ups attaining as much as 5.4 while CBL speed-ups reach at most 1.4 for an 8-node cluster. Overall, these results suggest that designers of cluster-based ODBMS should consider DSMIO as an efficient option for developing future projects in the field.