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Unifying data and replica placement for data-intensive services in geographically distributed clouds

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Abstract
The increased reliance of data management applications on cloud computing technologies has rendered research in identifying solutions to the data placement problem to be of paramount importance. The objective of the classical data placement problem is to optimally partition, while also allowing for replication, the set of data-items into distributed data centers to minimize the overall network communication cost. Despite significant advancement in data placement research, replica placement has seldom been studied in unison with data placement. More specifically, most of the existing solutions employ a two-phase approach: 1) data placement, followed by 2) replication. Replication should however be seen as an integral part of data placement, and should be studied as a joint optimization problem with the latter. In this paper, we propose a unified paradigm of data placement, called CPR, which combines data placement and replication of data-intensive services into geographically distributed clouds as a joint optimization problem. Underneath CPR, lies an overlapping correlation clustering algorithm capable of assigning a data-item to multiple data centers, thereby enabling us to jointly solve data placement and replication. Experiments on a real-world trace-based online social network dataset show that CPR is effective and scalable. Empirically, it is approximate to 35% better in efficacy on the evaluated metrics, while being up to 8 times faster in execution time when compared to state-of-the-art techniques.
Keywords
Data Placement, Replica Placement, Geographically Distributed Clouds, Location-Based Services, Online Social Networks, Scalability, Overlapping Clustering, LGORITHM, NETWORK

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MLA
Atrey, Ankita, et al. “Unifying Data and Replica Placement for Data-Intensive Services in Geographically Distributed Clouds.” CLOSER: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICES SCIENCE, edited by V. M. Munoz et al., Scitepress, 2019, pp. 25–36, doi:10.5220/0007613400250036.
APA
Atrey, A., Van Seghbroeck, G., Mora, H., De Turck, F., & Volckaert, B. (2019). Unifying data and replica placement for data-intensive services in geographically distributed clouds. In V. M. Munoz, D. Ferguson, M. Helfert, & C. Pahl (Eds.), CLOSER: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICES SCIENCE (pp. 25–36). Heraklion, Greece: Scitepress. https://doi.org/10.5220/0007613400250036
Chicago author-date
Atrey, Ankita, Gregory Van Seghbroeck, Higinio Mora, Filip De Turck, and Bruno Volckaert. 2019. “Unifying Data and Replica Placement for Data-Intensive Services in Geographically Distributed Clouds.” In CLOSER: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICES SCIENCE, edited by V. M. Munoz, D. Ferguson, M. Helfert, and C. Pahl, 25–36. Scitepress. https://doi.org/10.5220/0007613400250036.
Chicago author-date (all authors)
Atrey, Ankita, Gregory Van Seghbroeck, Higinio Mora, Filip De Turck, and Bruno Volckaert. 2019. “Unifying Data and Replica Placement for Data-Intensive Services in Geographically Distributed Clouds.” In CLOSER: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICES SCIENCE, ed by. V. M. Munoz, D. Ferguson, M. Helfert, and C. Pahl, 25–36. Scitepress. doi:10.5220/0007613400250036.
Vancouver
1.
Atrey A, Van Seghbroeck G, Mora H, De Turck F, Volckaert B. Unifying data and replica placement for data-intensive services in geographically distributed clouds. In: Munoz VM, Ferguson D, Helfert M, Pahl C, editors. CLOSER: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICES SCIENCE. Scitepress; 2019. p. 25–36.
IEEE
[1]
A. Atrey, G. Van Seghbroeck, H. Mora, F. De Turck, and B. Volckaert, “Unifying data and replica placement for data-intensive services in geographically distributed clouds,” in CLOSER: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICES SCIENCE, Heraklion, Greece, 2019, pp. 25–36.
@inproceedings{8619260,
  abstract     = {{The increased reliance of data management applications on cloud computing technologies has rendered research in identifying solutions to the data placement problem to be of paramount importance. The objective of the classical data placement problem is to optimally partition, while also allowing for replication, the set of data-items into distributed data centers to minimize the overall network communication cost. Despite significant advancement in data placement research, replica placement has seldom been studied in unison with data placement. More specifically, most of the existing solutions employ a two-phase approach: 1) data placement, followed by 2) replication. Replication should however be seen as an integral part of data placement, and should be studied as a joint optimization problem with the latter. In this paper, we propose a unified paradigm of data placement, called CPR, which combines data placement and replication of data-intensive services into geographically distributed clouds as a joint optimization problem. Underneath CPR, lies an overlapping correlation clustering algorithm capable of assigning a data-item to multiple data centers, thereby enabling us to jointly solve data placement and replication. Experiments on a real-world trace-based online social network dataset show that CPR is effective and scalable. Empirically, it is approximate to 35% better in efficacy on the evaluated metrics, while being up to 8 times faster in execution time when compared to state-of-the-art techniques.}},
  author       = {{Atrey, Ankita and Van Seghbroeck, Gregory and Mora, Higinio and De Turck, Filip and Volckaert, Bruno}},
  booktitle    = {{CLOSER: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICES SCIENCE}},
  editor       = {{Munoz, V. M. and Ferguson, D. and Helfert, M. and Pahl, C.}},
  isbn         = {{9789897583650}},
  keywords     = {{Data Placement,Replica Placement,Geographically Distributed Clouds,Location-Based Services,Online Social Networks,Scalability,Overlapping Clustering,LGORITHM,NETWORK}},
  language     = {{eng}},
  location     = {{Heraklion, Greece}},
  pages        = {{25--36}},
  publisher    = {{Scitepress}},
  title        = {{Unifying data and replica placement for data-intensive services in geographically distributed clouds}},
  url          = {{http://dx.doi.org/10.5220/0007613400250036}},
  year         = {{2019}},
}

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