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Cross-cloud mapreduce for big data

journal contribution
posted on 2015-08-28, 00:00 authored by P Li, S Guo, Shui Yu, W Zhuang
MapReduce plays a critical role as a leading framework for big data analytics. In this paper, we consider a geodistributed cloud architecture that provides MapReduce services based on the big data collected from end users all over the world. Existing work handles MapReduce jobs by a traditional computation-centric approach that all input data distributed in multiple clouds are aggregated to a virtual cluster that resides in a single cloud. Its poor efficiency and high cost for big data support motivate us to propose a novel data-centric architecture with three key techniques, namely, cross-cloud virtual cluster, data-centric job placement, and network coding based traffic routing. Our design leads to an optimization framework with the objective of minimizing both computation and transmission cost for running a set of MapReduce jobs in geo-distributed clouds. We further design a parallel algorithm by decomposing the original large-scale problem into several distributively solvable subproblems that are coordinated by a high-level master problem. Finally, we conduct real-world experiments and extensive simulations to show that our proposal significantly outperforms the existing works.

History

Journal

IEEE transactions on cloud computing

Pagination

1 - 13

Publisher

Institute of Electrical and Electronics Engineers

Location

Piscataway, N.J.

eISSN

2168-7161

Language

eng

Notes

In press

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2015, IEEE