Logical Time in Distributed Computing Systems
Computer - Distributed computing systems: separate resources acting as one
An efficient implementation of vector clocks
Information Processing Letters
PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
A fully dynamic algorithm for maintaining the transitive closure
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Time, clocks, and the ordering of events in a distributed system
Communications of the ACM
The Java Language Specification
The Java Language Specification
A framework algorithm for dynamic, centralized dimension-bounded timestamps
CASCON '00 Proceedings of the 2000 conference of the Centre for Advanced Studies on Collaborative research
A Hierarchical Cluster Algorithm for Dynamic, Centralized Timestamps
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
Detecting causal relationships in distributed computations: in search of the holy grail
Distributed Computing
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Distributed-system observation tools require an efficient data structure to store and query the partial-order of execution. Such data structures typically use vector timestamps to efficiently answer precedence queries. Many current vector-timestamp algorithms either have a poor time/space complexity tradeoff or are static. This limits the scalability of such observation tools. One algorithm, centralized hierarchical cluster timestamps, has potentially a good time/space tradeoff provided that the clusters accurately capture communication locality. However, that algorithm, as described, uses pre-determined, contiguous clusters. In this paper we extend that algorithm to enable a dynamic selection of clusters. We present experimental results that demonstrate that our extension is more stable with cluster size and provides timestamps whose average size is consistently superior to the pre-determined cluster approach.