TY - GEN
T1 - Brief announcement
T2 - 37th ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, PODC 2018
AU - Xiang, Zhuolun
AU - Vaidya, Nitin H.
N1 - Publisher Copyright:
© 2018 Copyright held by the owner/author(s).
PY - 2018/7/23
Y1 - 2018/7/23
N2 - Distributed shared memory systems maintain multiple replicas of the shared memory registers. Maintaining causal consistency in such systems has received significant attention in the past. However, much of the previous literature focuses on full replication wherein each replica stores a copy of all the registers in the shared memory. In this paper, we investigate causal consistency in partially replicated systems, wherein each replica may store only a subset of the shared data. To achieve causal consistency, it is necessary to ensure that, before an update is performed at any given replica, all causally preceding updates must also be performed. Achieving this goal requires some mechanism to track causal dependencies. In the context of full replication, this goal is often achieved using vector timestamps, with the number of vector elements being equal to the number of replicas. Building on the past work, this paper makes two key contributions: • For a family of algorithms for maintaining causal consistency, we present necessary conditions on the metadata (which we refer as a timestamp) that must be maintained by each replica. • We present an algorithm for achieving causal consistency using a timestamp that matches one of the necessary conditions referred above, thus showing that the condition is necessary and sufficient both.
AB - Distributed shared memory systems maintain multiple replicas of the shared memory registers. Maintaining causal consistency in such systems has received significant attention in the past. However, much of the previous literature focuses on full replication wherein each replica stores a copy of all the registers in the shared memory. In this paper, we investigate causal consistency in partially replicated systems, wherein each replica may store only a subset of the shared data. To achieve causal consistency, it is necessary to ensure that, before an update is performed at any given replica, all causally preceding updates must also be performed. Achieving this goal requires some mechanism to track causal dependencies. In the context of full replication, this goal is often achieved using vector timestamps, with the number of vector elements being equal to the number of replicas. Building on the past work, this paper makes two key contributions: • For a family of algorithms for maintaining causal consistency, we present necessary conditions on the metadata (which we refer as a timestamp) that must be maintained by each replica. • We present an algorithm for achieving causal consistency using a timestamp that matches one of the necessary conditions referred above, thus showing that the condition is necessary and sufficient both.
KW - Causal consistency
KW - Distributed shared memory
KW - Lower bounds
KW - Partial replication
KW - Timestamps
UR - http://www.scopus.com/inward/record.url?scp=85052484123&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052484123&partnerID=8YFLogxK
U2 - 10.1145/3212734.3212790
DO - 10.1145/3212734.3212790
M3 - Conference contribution
AN - SCOPUS:85052484123
SN - 9781450357951
T3 - Proceedings of the Annual ACM Symposium on Principles of Distributed Computing
SP - 273
EP - 275
BT - PODC 2018 - Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing
PB - Association for Computing Machinery
Y2 - 23 July 2018 through 27 July 2018
ER -