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Authors: Ankita Atrey ; Gregory Van Seghbroeck ; Bruno Volckaert and Filip De Turck

Affiliation: UGent, Belgium

ISBN: 978-989-758-295-0

Keyword(s): Data Placement, Geo-distributed Clouds, Location-based Services, Online Social Networks, Scalability, Spectral Clustering, Hypergraphs, Approximation.

Abstract: The advent of big data analytics and cloud computing technologies has resulted in wide-spread research in finding solutions to the data placement problem, which aims at properly placing the data items into distributed datacenters. Although traditional schemes of uniformly partitioning the data into distributed nodes is the defacto standard for many popular distributed data stores like HDFS or Cassandra, these methods may cause network congestion for data-intensive services, thereby affecting the system throughput. This is because as opposed to MapReduce style workloads, data-intensive services require access to multiple datasets within each transaction. In this paper, we propose a scalable method for performing data placement of data-intensive services into geographically distributed clouds. The proposed algorithm partitions a set of data-items into geodistributed clouds using spectral clustering on hypergraphs. Additionally, our spectral clustering algorithm leverages randomiz ed techniques for obtaining low-rank approximations of the hypergraph matrix, thereby facilitating superior scalability for computation of the spectra of the hypergraph laplacian. Experiments on a real-world trace-based online social network dataset show that the proposed algorithm is effective, efficient, and scalable. Empirically, it is comparable or even better (in certain scenarios) in efficacy on the evaluated metrics, while being up to 10 times faster in running time when compared to state-of-the-art techniques. (More)

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Paper citation in several formats:
Atrey A., Van Seghbroeck G., Volckaert B. and De Turck F. (2018). Scalable Data Placement of Data-intensive Services in Geo-distributed Clouds.In Proceedings of the 8th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-295-0, pages 497-508. DOI: 10.5220/0006767504970508

@conference{closer18,
author={Ankita Atrey and Gregory Van Seghbroeck and Bruno Volckaert and Filip De Turck},
title={Scalable Data Placement of Data-intensive Services in Geo-distributed Clouds},
booktitle={Proceedings of the 8th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2018},
pages={497-508},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006767504970508},
isbn={978-989-758-295-0},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - Scalable Data Placement of Data-intensive Services in Geo-distributed Clouds
SN - 978-989-758-295-0
AU - Atrey A.
AU - Van Seghbroeck G.
AU - Volckaert B.
AU - De Turck F.
PY - 2018
SP - 497
EP - 508
DO - 10.5220/0006767504970508

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