Cost-effective and traffic-optimal data placement strategy for cloud-based online social networks

Lei Zhang; Xuejun Li; Hourieh Khalajzadeh; Yan Yang; Ruiyue Zhu; Xia Ji; Chuanhui Ju; Yun Yang

doi:10.1109/CSCWD.2018.8465343

Cost-effective and traffic-optimal data placement strategy for cloud-based online social networks

Lei Zhang, Xuejun Li, Hourieh Khalajzadeh, Yan Yang, Ruiyue Zhu, Xia Ji, Chuanhui Ju, Yun Yang

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

13 Citations (Scopus)

Abstract

Cloud-based Online Social Networks (OSNs) make it easier for geographically dispersed users to communicate with each other. These users not only demand to quickly access their own data but also hope to access their friends' data with low latency. In order to solve the problem, it is necessary to design a replica placement strategy to manage data on large-scale social networks and reduce the data storage costs while meeting the access latency requirement. In this paper, we propose a novel genetic algorithm-based data placement strategy to find an optimal number of replicas for each user's data and their optimal location. The method can reduce the inter-server traffic load across servers and ensure that users can access data in a tolerable time. Experiments with real Facebook dataset demonstrate that our data placement strategy can significantly reduce the cost of data storage and inter-server traffic.

Original language	English
Title of host publication	Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD 2018)
Subtitle of host publication	May 9-11, 2018, Nanjing, China
Editors	Weiming Shen, Junzhou Luo, Jean-Paul Barthès, Fang Dong, Jinghui Zhang, Haibin Zhu
Place of Publication	Danvers MA USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	110-115
Number of pages	6
ISBN (Electronic)	9781538614815, 9781538614839
ISBN (Print)	9781538614822
DOIs	https://doi.org/10.1109/CSCWD.2018.8465343
Publication status	Published - 13 Sept 2018
Externally published	Yes
Event	International Conference on Computer Supported Cooperative Work in Design 2018 - Nanjing, China Duration: 9 May 2018 → 11 May 2018 Conference number: 22nd http://2018.cscwd.org/

Conference

Conference	International Conference on Computer Supported Cooperative Work in Design 2018
Abbreviated title	CSCWD 2018
Country/Territory	China
City	Nanjing
Period	9/05/18 → 11/05/18
Internet address	http://2018.cscwd.org/

Keywords

Access latency
Data placement
Genetic algorithm
Inter-server traffic
Social network

Access to Document

10.1109/CSCWD.2018.8465343

Cite this

Zhang, L., Li, X., Khalajzadeh, H., Yang, Y., Zhu, R., Ji, X., Ju, C., & Yang, Y. (2018). Cost-effective and traffic-optimal data placement strategy for cloud-based online social networks. In W. Shen, J. Luo, J.-P. Barthès, F. Dong, J. Zhang, & H. Zhu (Eds.), Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD 2018): May 9-11, 2018, Nanjing, China (pp. 110-115). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CSCWD.2018.8465343

Zhang, Lei ; Li, Xuejun ; Khalajzadeh, Hourieh et al. / Cost-effective and traffic-optimal data placement strategy for cloud-based online social networks. Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD 2018): May 9-11, 2018, Nanjing, China. editor / Weiming Shen ; Junzhou Luo ; Jean-Paul Barthès ; Fang Dong ; Jinghui Zhang ; Haibin Zhu. Danvers MA USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 110-115

@inproceedings{d5168cad655f4a58a61bad77026dcb08,

title = "Cost-effective and traffic-optimal data placement strategy for cloud-based online social networks",

abstract = "Cloud-based Online Social Networks (OSNs) make it easier for geographically dispersed users to communicate with each other. These users not only demand to quickly access their own data but also hope to access their friends' data with low latency. In order to solve the problem, it is necessary to design a replica placement strategy to manage data on large-scale social networks and reduce the data storage costs while meeting the access latency requirement. In this paper, we propose a novel genetic algorithm-based data placement strategy to find an optimal number of replicas for each user's data and their optimal location. The method can reduce the inter-server traffic load across servers and ensure that users can access data in a tolerable time. Experiments with real Facebook dataset demonstrate that our data placement strategy can significantly reduce the cost of data storage and inter-server traffic.",

keywords = "Access latency, Data placement, Genetic algorithm, Inter-server traffic, Social network",

author = "Lei Zhang and Xuejun Li and Hourieh Khalajzadeh and Yan Yang and Ruiyue Zhu and Xia Ji and Chuanhui Ju and Yun Yang",

year = "2018",

month = sep,

day = "13",

doi = "10.1109/CSCWD.2018.8465343",

language = "English",

isbn = "9781538614822",

pages = "110--115",

editor = "Weiming Shen and Junzhou Luo and Jean-Paul Barth{\`e}s and Fang Dong and Jinghui Zhang and Haibin Zhu",

booktitle = "Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD 2018)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "International Conference on Computer Supported Cooperative Work in Design 2018, CSCWD 2018 ; Conference date: 09-05-2018 Through 11-05-2018",

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Zhang, L, Li, X, Khalajzadeh, H, Yang, Y, Zhu, R, Ji, X, Ju, C & Yang, Y 2018, Cost-effective and traffic-optimal data placement strategy for cloud-based online social networks. in W Shen, J Luo, J-P Barthès, F Dong, J Zhang & H Zhu (eds), Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD 2018): May 9-11, 2018, Nanjing, China. IEEE, Institute of Electrical and Electronics Engineers, Danvers MA USA, pp. 110-115, International Conference on Computer Supported Cooperative Work in Design 2018, Nanjing, China, 9/05/18. https://doi.org/10.1109/CSCWD.2018.8465343

Cost-effective and traffic-optimal data placement strategy for cloud-based online social networks. / Zhang, Lei; Li, Xuejun; Khalajzadeh, Hourieh et al.
Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD 2018): May 9-11, 2018, Nanjing, China. ed. / Weiming Shen; Junzhou Luo; Jean-Paul Barthès; Fang Dong; Jinghui Zhang; Haibin Zhu. Danvers MA USA: IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 110-115.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Cost-effective and traffic-optimal data placement strategy for cloud-based online social networks

AU - Zhang, Lei

AU - Li, Xuejun

AU - Khalajzadeh, Hourieh

AU - Yang, Yan

AU - Zhu, Ruiyue

AU - Ji, Xia

AU - Ju, Chuanhui

AU - Yang, Yun

N1 - Conference code: 22nd

PY - 2018/9/13

Y1 - 2018/9/13

N2 - Cloud-based Online Social Networks (OSNs) make it easier for geographically dispersed users to communicate with each other. These users not only demand to quickly access their own data but also hope to access their friends' data with low latency. In order to solve the problem, it is necessary to design a replica placement strategy to manage data on large-scale social networks and reduce the data storage costs while meeting the access latency requirement. In this paper, we propose a novel genetic algorithm-based data placement strategy to find an optimal number of replicas for each user's data and their optimal location. The method can reduce the inter-server traffic load across servers and ensure that users can access data in a tolerable time. Experiments with real Facebook dataset demonstrate that our data placement strategy can significantly reduce the cost of data storage and inter-server traffic.

AB - Cloud-based Online Social Networks (OSNs) make it easier for geographically dispersed users to communicate with each other. These users not only demand to quickly access their own data but also hope to access their friends' data with low latency. In order to solve the problem, it is necessary to design a replica placement strategy to manage data on large-scale social networks and reduce the data storage costs while meeting the access latency requirement. In this paper, we propose a novel genetic algorithm-based data placement strategy to find an optimal number of replicas for each user's data and their optimal location. The method can reduce the inter-server traffic load across servers and ensure that users can access data in a tolerable time. Experiments with real Facebook dataset demonstrate that our data placement strategy can significantly reduce the cost of data storage and inter-server traffic.

KW - Access latency

KW - Data placement

KW - Genetic algorithm

KW - Inter-server traffic

KW - Social network

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DO - 10.1109/CSCWD.2018.8465343

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SN - 9781538614822

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EP - 115

BT - Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD 2018)

A2 - Shen, Weiming

A2 - Luo, Junzhou

A2 - Barthès, Jean-Paul

A2 - Dong, Fang

A2 - Zhang, Jinghui

A2 - Zhu, Haibin

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Danvers MA USA

T2 - International Conference on Computer Supported Cooperative Work in Design 2018

Y2 - 9 May 2018 through 11 May 2018

ER -

Zhang L, Li X, Khalajzadeh H, Yang Y, Zhu R, Ji X et al. Cost-effective and traffic-optimal data placement strategy for cloud-based online social networks. In Shen W, Luo J, Barthès JP, Dong F, Zhang J, Zhu H, editors, Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD 2018): May 9-11, 2018, Nanjing, China. Danvers MA USA: IEEE, Institute of Electrical and Electronics Engineers. 2018. p. 110-115 doi: 10.1109/CSCWD.2018.8465343

Cost-effective and traffic-optimal data placement strategy for cloud-based online social networks

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