SDTP: Accelerating Wide-Area Data Analytics With Simultaneous Data Transfer and Processing

For the efficient analysis of geo-distributed datasets, cloud providers implement data-parallel jobs across geo-distributed sites (e.g., datacenters and edge clusters), which are generally interconnected by wide-area network links. However, current state-of-the-art geo-distributed data analytic meth...

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Bibliographic Details
Published in:IEEE transactions on cloud computing 2023-01, Vol.11 (1), p.911-926
Main Authors: Chen, Yiting, Luo, Lailong, Guo, Deke, Rottenstreich, Ori, Wu, Jie
Format: Article
Language:English
Subjects:
Bandwidth
Bandwidths
Computation
Data analysis
Data centers
Data transfer (computers)
dynamic network
job parallelism
job response time
Mathematical analysis
Parallel processing
Silicon
Task analysis
task scheduling
Time factors
Wide area networks
Wide-area data analytics
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Summary:For the efficient analysis of geo-distributed datasets, cloud providers implement data-parallel jobs across geo-distributed sites (e.g., datacenters and edge clusters), which are generally interconnected by wide-area network links. However, current state-of-the-art geo-distributed data analytic methods fail to make full use of the available network and computing resources. The main reason is that such geo-distributed methods must wait for bottleneck sites to complete the corresponding transmission and computation in each phase. Furthermore, such geo-distributed methods may be impractical to the network bandwidth dynamicity and diverse job parallelism. To this end, we propose a Simultaneous Data Transfer and Processing (SDTP) mechanism to accelerate wide-area data analytics, with the joint consideration of network bandwidth dynamics and job parallelism. In the SDTP, a site can execute the computation, provided that it obtains the required input data. As a result, the input data loading, map, shuffle, and reduce phases at each site need not wait for the completion of the previous phases of other sites. We further improve the SDTP method by offering more accurate time estimation and generalizing the mechanism to dynamic situations. The trace-driven results demonstrate that SDTP can improve the wide-area analytic job response time by 19% to 72% compared to other methods.
ISSN:2168-7161
2372-0018
DOI:10.1109/TCC.2021.3119991