Citation Export
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Hamandawana, Prince | - |
| dc.contributor.author | Mativenga, Ronnie | - |
| dc.contributor.author | Kwon, Se Jin | - |
| dc.contributor.author | Chung, Tae Sun | - |
| dc.date.issued | 2018-10-29 | - |
| dc.identifier.issn | 1552-5244 | - |
| dc.identifier.uri | https://aurora.ajou.ac.kr/handle/2018.oak/36269 | - |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85057269037&origin=inward | - |
| dc.description.abstract | A lot of previous works on Map-Reduce improved job completion performance through implementing additional instrumentation modules which collects system level information for making scheduling decisions. However the extra instrumentation may not scale well with increasing number of task-trackers. To this end, we design PADS, a lightweight scheduler which uses time prediction to schedule tasks without additional instrumentation modules. Results shows PADS improves performance by 6%, 12%, and 9% as compared to ESAMR, LA, and DDAS respectively. | - |
| dc.language.iso | eng | - |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
| dc.subject.mesh | Dynamic scheduling | - |
| dc.subject.mesh | Hadoop | - |
| dc.subject.mesh | Heterogeneity | - |
| dc.subject.mesh | Heterogeneous clusters | - |
| dc.subject.mesh | Job completion | - |
| dc.subject.mesh | Map-reduce | - |
| dc.subject.mesh | Scheduling decisions | - |
| dc.subject.mesh | Time predictions | - |
| dc.title | PADS: Performance-Aware Dynamic Scheduling for Effective MapReduce Computation in Heterogeneous Clusters | - |
| dc.type | Conference | - |
| dc.citation.conferenceDate | 2018.9.10. ~ 2018.9.13. | - |
| dc.citation.conferenceName | 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018 | - |
| dc.citation.edition | Proceedings - 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018 | - |
| dc.citation.endPage | 161 | - |
| dc.citation.startPage | 160 | - |
| dc.citation.title | Proceedings - IEEE International Conference on Cluster Computing, ICCC | - |
| dc.citation.volume | 2018-September | - |
| dc.identifier.bibliographicCitation | Proceedings - IEEE International Conference on Cluster Computing, ICCC, Vol.2018-September, pp.160-161 | - |
| dc.identifier.doi | 10.1109/cluster.2018.00032 | - |
| dc.identifier.scopusid | 2-s2.0-85057269037 | - |
| dc.subject.keyword | Hadoop | - |
| dc.subject.keyword | Heterogeneity | - |
| dc.subject.keyword | MapReduce | - |
| dc.subject.keyword | Scheduling | - |
| dc.type.other | Conference Paper | - |
| dc.description.isoa | false | - |
| dc.subject.subarea | Software | - |
| dc.subject.subarea | Hardware and Architecture | - |
| dc.subject.subarea | Signal Processing | - |
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