Cost-Effective Data Analytics across Multiple Cloud Regions

被引：0

作者：

Shu, Junyi ^{[1
]}

Jin, Xin ^{[1
]}

Ma, Yun ^{[1
]}

Liu, Xuanzhe ^{[1
]}

Huang, Gang ^{[1
]}

机构：

[1] Peking Univ, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE 2021 SIGCOMM 2021 POSTER AND DEMO SESSIONS, SIGCOMM 2021 DEMOS AND POSTERS | 2024年

关键词：

data analytics; cost optimization; job scheduling; multi-cloud;

D O I：

10.1145/3472716.3472842

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We propose a cloud-native data analytics engine for processing data stored among geographically distributed cloud regions with reduced cost. A job is split into subtasks and placed across regions based on factors including prices of compute resources and data transmission. We present its architecture which leverages existing cloud infrastructures and discuss major challenges of its system design. Preliminary experiments show that the cost is reduced by 15.1% for a decision support query on a four-region public cloud setup.

引用

页码：1 / 3

页数：3

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