Cross-Rack-Aware Updates in Erasure-Coded Data Centers
Refereed conference paper presented and published in conference proceedings

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AbstractThe update performance in erasure-coded data centers is often bottlenecked by the constrained cross-rack bandwidth. We propose CAU, a cross-rack-aware update mechanism that aims to mitigate the cross-rack update traffic in erasure-coded data centers. CAU builds on three design elements: (i) selective parity updates, which select the appropriate parity update approach based on the update pattern and the data layout to reduce the cross-rack update traffic; (ii) data grouping, which relocates and groups updated data chunks in the same rack to further reduce the cross-rack update traffic; and (iii) interim replication, which stores a temporary replica for each newly updated data chunk. We evaluate CAU via trace-driven analysis, local cluster experiments, and Amazon EC2 experiments. We show that CAU enhances state-of-the-arts by mitigating the cross-rack update traffic as well as maintaining high update performance in both local cluster and geo-distributed environments.
All Author(s) ListZhirong Shen, Patrick P. C. Lee
Name of Conference47th International Conference on Parallel Processing (ICPP 2018)
Start Date of Conference13/08/2018
End Date of Conference16/08/2018
Place of ConferenceEugene, Oregon
Country/Region of ConferenceUnited States of America
Proceedings TitleProceedings of the 47th International Conference on Parallel Processing (ICPP 2018)
Article number80
LanguagesEnglish-United States

Last updated on 2021-24-07 at 01:21