Name
Abstract | ||
|---|---|---|
To avoid the high cost of continuous connectivity, a class of mobile applications employs replicas of shared data that are periodically updated. Updates to these replicas are typically performed on a client-by-client basis--that is, the server individually computes and transmits updates to each client--limiting scalability. By basing updates on replica groups (instead of clients), however, update generation complexity is no longer bound by client population size. Clients then download updates of pertinent groups. Proper group design reduces redundancies in server processing, disk usage and bandwidth usage, and dimininishes the tie between the complexity of updating replicas and the size of the client population. In this paper, we expand on previous work done on group design, include a detailed I/O cost model for update generation, and propose a heuristic-based greedy algorithm for group computation. Experimental results with an adapted commercial replication system demonstrate a significant increase in overall scalability over the client-centric approach. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1145/502585.502661 | CIKM |
Keywords | Field | DocType |
replica maintenance,mobile databases,bandwidth usage,group computation,group design,proper group design,disk usage,client population,o cost model,replica group,client population size,pertinent group,distributed database,greedy algorithm,mobile database,population size,distributed databases | Replica,Population,Data mining,Heuristic,Computer science,Greedy algorithm,Mobile database,Bandwidth (signal processing),Distributed database,Scalability,Distributed computing | Conference |
ISBN | Citations | PageRank |
1-58113-436-3 | 5 | 0.48 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wai Gen Yee | 1 | 301 | 27.33 |
| Michael J. Donahoo | 2 | 137 | 52.31 |
| Edward Omiecinski | 3 | 1551 | 401.73 |
| Shamkant Navathe | 4 | 4563 | 2112.44 |