Name
Abstract | ||
|---|---|---|
Practical deployments of information agents can suffer from sub-optimal performance and scalability for a number of reasons. In the case of web-based information integration, for example, data sources are remote and their latency can have a substantial effect on overall execution performance. Scalability can also be poor, since concurrent queries can cause multiple, simultaneous remote data retrievals (often of the same information), quickly consuming available bandwidth. The frequency of remote retrievals also makes such agents inherently I/O-bound, wasting CPU cycles. One way of optimizing execution in such scenarios is to engage in speculative execution. Tasks likely to be executed in the future can be performed in advance, such as when an agent is I/O-bound. Correctly guessing can be profitable - the overall end-to-end application could perform faster, bandwidth could be conserved, and the CPU could be scheduled more optimally. Still, designing a technique for speculative |
Year | Venue | Keywords |
|---|---|---|
2000 | AAAI/IAAI | information agents,speculative execution,data retrieval,information integration,profitability |
Field | DocType | ISBN |
Speculation,Information integration,Central processing unit,Latency (engineering),Computer science,Speculative execution,Bandwidth (signal processing),Instruction cycle,Distributed computing,Scalability | Conference | 0-262-51112-6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Greg Barish | 1 | 168 | 43.67 |
| Craig A. Knoblock | 2 | 5229 | 680.57 |
| Steven Minton | 3 | 3473 | 536.74 |