Bohr: Similarity Aware Geo-distributed Data Analytics

Authors: 

Hangyu Li, Hong Xu, and Sarana Nutanong, City University of Hong Kong

Abstract: 

We propose Bohr, a similarity aware geo-distributed data analytics system that minimizes query completion time. The key idea is to exploit similarity between data in different data centers (DCs), and transfer similar data from the bottleneck DC to other sites with more WAN bandwidth. Though these sites have more input data to process, these data are more similar and can be more efficiently aggregated by the combiner to reduce the intermediate data that needs to be shuffled across the WAN. Thus our similarity aware approach reduces the shuffle time and in turn the query completion time (QCT).

We design and implement Bohr based on OLAP data cubes to perform efficient similarity checking among datasets in different sites. Evaluation across ten sites of AWS EC2 shows that Bohr decreases the QCT by 30% compared to state-of-the-art solutions.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

BibTeX
@inproceedings {203312,
author = {Hangyu Li and Hong Xu and Sarana Nutanong},
title = {Bohr: Similarity Aware Geo-distributed Data Analytics},
booktitle = {9th {USENIX} Workshop on Hot Topics in Cloud Computing (HotCloud 17)},
year = {2017},
address = {Santa Clara, CA},
url = {https://www.usenix.org/conference/hotcloud17/program/presentation/li},
publisher = {{USENIX} Association},
}