TY - GEN
T1 - Virtual full replication by adaptive segmentation
AU - Mathiason, Gunnar
AU - Andler, Sten F.
AU - Son, Sang H.
PY - 2007
Y1 - 2007
N2 - We propose Virtual Full Replication by Adaptive segmentation (ViFuR-A), and evaluate its ability to maintain scalability in a replicated real-time database. With full replication and eventual consistency, transaction timeliness becomes independent of network delays for all transactions. However, full replication does not scale well, since all updates must be replicated to all nodes, also when data is needed only at a subset of the nodes. With Virtual Full Replication that adapts to actual data needs, resource usage can be bounded and the database can be made scalable. We propose a scheme for adaptive segmentation that detects new data needs and adapts replication. The scheme includes an architecture, a scalable protocol and a replicated directory service that together maintains scalability. We show that adaptive segmentation bounds the required storage at a significantly lower level compared to static segmentation, for a typical workload where the data needs change repeatedly. Adaptation time can be kept constant for the workload when there are sufficient resources. Also, the storage is constant with an increasing amount of nodes and linear with an increasing rate of change to data needs.
AB - We propose Virtual Full Replication by Adaptive segmentation (ViFuR-A), and evaluate its ability to maintain scalability in a replicated real-time database. With full replication and eventual consistency, transaction timeliness becomes independent of network delays for all transactions. However, full replication does not scale well, since all updates must be replicated to all nodes, also when data is needed only at a subset of the nodes. With Virtual Full Replication that adapts to actual data needs, resource usage can be bounded and the database can be made scalable. We propose a scheme for adaptive segmentation that detects new data needs and adapts replication. The scheme includes an architecture, a scalable protocol and a replicated directory service that together maintains scalability. We show that adaptive segmentation bounds the required storage at a significantly lower level compared to static segmentation, for a typical workload where the data needs change repeatedly. Adaptation time can be kept constant for the workload when there are sufficient resources. Also, the storage is constant with an increasing amount of nodes and linear with an increasing rate of change to data needs.
UR - http://www.scopus.com/inward/record.url?scp=46449133901&partnerID=8YFLogxK
U2 - 10.1109/RTCSA.2007.72
DO - 10.1109/RTCSA.2007.72
M3 - Conference contribution
AN - SCOPUS:46449133901
SN - 0769529755
SN - 9780769529752
T3 - Proceedings - 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2007
SP - 327
EP - 337
BT - Proceedings - 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2007
T2 - 4296821
Y2 - 21 August 2007 through 24 August 2007
ER -