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PADS: Performance-Aware Dynamic Scheduling for Effective MapReduce Computation in Heterogeneous Clusters
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Publication Year
2018-10-29
Journal
Proceedings - IEEE International Conference on Cluster Computing, ICCC
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings - IEEE International Conference on Cluster Computing, ICCC, Vol.2018-September, pp.160-161
Keyword
HadoopHeterogeneityMapReduceScheduling
Mesh Keyword
Dynamic schedulingHadoopHeterogeneityHeterogeneous clustersJob completionMap-reduceScheduling decisionsTime predictions
All Science Classification Codes (ASJC)
SoftwareHardware and ArchitectureSignal Processing
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.
ISSN
1552-5244
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36269
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85057269037&origin=inward
DOI
https://doi.org/10.1109/cluster.2018.00032
Type
Conference
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HAMANDAWANA PRINCEHAMANDAWANA, PRINCE
Department of Software and Computer Engineering
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