Applied combinatorics
How to assign votes in a distributed system
Journal of the ACM (JACM)
Efficient decentralized consensus protocols
IEEE Transactions on Software Engineering
Adaptive load sharing in homogeneous distributed systems
IEEE Transactions on Software Engineering
Applications of combinatorial designs in computer science
ACM Computing Surveys (CSUR)
A N algorithm for mutual exclusion in decentralized systems
ACM Transactions on Computer Systems (TOCS)
A Majority consensus approach to concurrency control for multiple copy databases
ACM Transactions on Database Systems (TODS)
Time, clocks, and the ordering of events in a distributed system
Communications of the ACM
The Grid Protocol: A High Performance Scheme for Maintaining Replicated Data
Proceedings of the Sixth International Conference on Data Engineering
Weighted voting for replicated data
SOSP '79 Proceedings of the seventh ACM symposium on Operating systems principles
A Single Server Priority Queue With Server Failures and Queue Flushing
A Single Server Priority Queue With Server Failures and Queue Flushing
Theory, Volume 1, Queueing Systems
Theory, Volume 1, Queueing Systems
Hi-index | 0.01 |
A well-known algorithm for updating multiple copies is the Thomas majority consensus algorithm. This algorithm, before performing an update, needs to obtain permission from a majority of the nodes in the system. We study the response-time behavior of a symmetric (each node seeks permission from the same number of other nodes and each node receives requests for update permission from the same number of other nodes) distributed update-synchronization algorithm where nodes need to obtain permission from only O(/spl radic/N) (N being the number of database copies) other nodes before performing an update. The algorithm we use is an adaptation of Maekawa's O(/spl radic/N) distributed mutual exclusion algorithm to multiple-copy update-synchronization. This increase in the efficiency of the update-synchronization algorithm enhances performance in two ways. First, the reduction in transaction service time reduces the response time. Second, for a given arrival rate of transactions, the decrease in response time reduces the number of waiting transactions in the system. This reduces the probability of conflict between transactions. To capture the interaction between the probability of conflict and the transaction response time, we define a new measure called the conflict response-time product. Based on the solution of a queueing model we show that optimizing this measure yields a different and more appropriate choice of system parameters than simply minimizing the mean transaction response time.