Name
Abstract | ||
|---|---|---|
Database engines often consume significant power during query processing activities, motivating researchers to investigate the redesign of their internals to minimize these overheads. While the prior literature has dealt exclusively with average power considerations, our focus here is on peak power consumption. We begin by profiling the peak power behavior of a representative suite of popular commercial database engines in benchmark query processing environments, and demonstrate that their consumption can often be substantial. Then, we develop a pipeline-based model of query execution plans that lends itself to accurately estimating peak power consumption, suggesting its gainful employment in server design and capacity planning. More potently, given a space of competing plan choices, it could help identify plans with attractive tradeoffs between peak-power and time-efficiency considerations, and we present sample instances of such tradeoffs. Finally, we discuss extensions of our modeling approach to inductive pipelines and multi-query workloads. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2247596.2247648 | EDBT |
Keywords | Field | DocType |
database engine,query processing activity,average power consideration,capacity planning,attractive tradeoffs,query execution plan,benchmark query processing environment,peak power behavior,peak power consumption,significant power | Data mining,Pipeline transport,Suite,Profiling (computer programming),Computer science,Gainful employment,Capacity planning,Database,Power consumption,Overhead (business) | Conference |
Citations | PageRank | References |
13 | 0.62 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mayuresh Kunjir | 1 | 13 | 0.62 |
| Puneet K. Birwa | 2 | 13 | 0.62 |
| Jayant R. Haritsa | 3 | 2004 | 228.38 |