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Temporal workload-aware replicated partitioning for social networks
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Turk, Ata, Selvitopi, R. Oguz, Ferhatosmanoglu, Hakan and Aykanat, Cevdet (2014) Temporal workload-aware replicated partitioning for social networks. IEEE Transactions on Knowledge and Data Engineering, 26 (11). pp. 2832-2845. doi:10.1109/TKDE.2014.2302291 ISSN 1041-4347.
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WRAP-temporal-workload-aware-partitioning-social-Ferhatosmanoglu-2014.pdf - Accepted Version - Requires a PDF viewer. Download (2143Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TKDE.2014.2302291
Abstract
Most frequent and expensive queries in social networks involve multi-user operations such as requesting the latest tweets or news-feeds of friends. The performance of such queries are heavily dependent on the data partitioning and replication methodologies adopted by the underlying systems. Existing solutions for data distribution in these systems involve hashor graph-based approaches that ignore the multi-way relations among data. In this work, we propose a novel data partitioning and selective replication method that utilizes the temporal information in prior workloads to predict future query patterns. Our method utilizes the social network structure and the temporality of the interactions among its users to construct a hypergraph that correctly models multi-user operations. It then performs simultaneous partitioning and replication of this hypergraph to reduce the query span while respecting load balance and I/O load constraints under replication. To test our model, we enhance the Cassandra NoSQL system to support selective replication and we implement a social network application (a Twitter clone) utilizing our enhanced Cassandra. We conduct experiments on a cloud computing environment (Amazon EC2) to test the developed systems. Comparison of the proposed method with hash- and enhanced graph-based schemes indicate that it significantly improves latency and throughput.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Hypergraphs, Social networks, Twitter (Firm), Microblogs | ||||||
Journal or Publication Title: | IEEE Transactions on Knowledge and Data Engineering | ||||||
Publisher: | IEEE Computer Society | ||||||
ISSN: | 1041-4347 | ||||||
Official Date: | 23 January 2014 | ||||||
Dates: |
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Volume: | 26 | ||||||
Number: | 11 | ||||||
Page Range: | pp. 2832-2845 | ||||||
DOI: | 10.1109/TKDE.2014.2302291 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 29 September 2017 | ||||||
Date of first compliant Open Access: | 29 September 2017 | ||||||
Funder: | Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, Türkiye Bilimler Akademisi | ||||||
Grant number: | EEAG-112E120 (Türkiye Bilimsel ve Teknolojik Araştırma Kurumu) |
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