Cited By
View all- Naseera S(2017)A survey on data replication strategies in a Data Grid environmentMultiagent and Grid Systems10.3233/MGS-16025312:4(253-269)Online publication date: 13-Jan-2017
Adaptive strength geo-replication strategy
Article No.: 3, Pages 1 - 4
Abstract
The amount of data being processed in Data Centres (DCs) keeps growing at an enormous rate so that full replication may start being impractical. The application of replication between DCs is used to increase data availability in the presence of site failures and to reduce latency by accessing the data closely located, if possible. This means that replicating the data only in some of the DCs is becoming more critical in order to reduce the costs of keeping the data (weakly) consistent while maintaining high availability (scalability) and low access costs. When data read and write request patterns change, then deciding which data should be replicated and where needs to be made dynamically. Given that the problem of finding an optimal replication schema in a general network has been shown to be NP-complete for the static case, so it is unlikely that there exists a general algorithm for an optimal solution to the dynamic problem.
We present here a new adaptive bio--inspired replication strategy, which is completely decentralised, adaptive, and event-driven, inspired on the Ant Colony algorithm.
References
[1]
C. L. Abad, Y. Lu, and R. H. Campbell. Dare: Adaptive data replication for efficient cluster scheduling. In Proceedings of the 2011 IEEE International Conference on Cluster Computing, CLUSTER '11, pages 159--168, Washington, DC, USA, 2011. IEEE Computer Society. ISBN 978-0-7695-4516-5. URL https://wiki.engr.illinois.edu/download/attachments/194990492/cluster11.pdf.
[2]
S. Abdul-Wahid, R. Andonie, J. Lemley, J. Schwing, and J. Widger. Adaptive distributed database replication through colonies of pogo ants. In Parallel and Distributed Processing Symposium, 2007. IPDPS 2007. IEEE International, pages 1--8, March 2007. URL http://www.cwu.edu/~andonie/MyPapers/IPDPS%20Long%20Beach%202007%20final.pdf.
[3]
P. M. G. Apers. Data allocation in distributed database systems. ACM Transactions on Database Systems, 13: 263--304, 1988.
[4]
I. Briquemont. Optimising client-side geo-replication with partially replicated data structures. Master's thesis, Louvainla-Neuve, September 2014. URL http://www.info.ucl.ac.be/~pvr/MemoireIwanBriquemont.pdf.
[5]
A. Chanthadavong. Internet of things to drive explosion of useful data: Emc. Technical report, ZDNet, April 2014. URL http://www.zdnet.com/internet-of-things-to-drive-explosion-of-useful-data-emc-7000028376.
[6]
Cisco. The zettabyte era-trends and analysis. Technical report, Cisco, June 2014. URL http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/VNI_Hyperconnectivity_WP.pdf.
[7]
X. Dong, J. Li, Z. Wu, D. Zhang, and J. Xu. On dynamic replication strategies in data service grids. In Object Oriented Real-Time Distributed Computing (ISORC), 2008 11th IEEE International Symposium on, pages 155--161, May 2008.
[8]
M. Dorigo. Optimization, Learning and Natural Algorithms. PhD thesis, Politecnico di Milano, Italy, 1992.
[9]
S. Goel and R. Buyya. Data replication strategies in wide area distributed systems. In R. G. Qiu, editor, Enterprise Service Computing: From Concept to Deployment, pages 211--241. Idea Group Inc., 2006. URL http://www.cloudbus.org/papers/DataReplicationInDSChapter2006.pdf.
[10]
R. Kingsy Grace and R. Manimegalai. Dynamic replica placement and selection strategies in data grids- a comprehensive survey. J. Parallel Distrib. Comput., 74(2): 2099--2108, Feb. 2013. ISSN 0743-7315. URL http://dx.doi.org/10.1016/j.jpdc.2013.10.009.
[11]
A. Liu, Q. Li, and L. Huang. Quality driven web services replication using directed acyclic graph coding. In A. Bouguettaya, M. Hauswirth, and L. Liu, editors, Web Information System Engineering -- WISE 2011, volume 6997 of Lecture Notes in Computer Science, pages 322--329. Springer Berlin Heidelberg, 2011. ISBN 978-3-642-24433-9. URL http://dx.doi.org/10.1007/978-3-642-24434-6_28.
[12]
T. Loukopoulos and I. Ahmad. Static and adaptive distributed data replication using genetic algorithms. J. Parallel Distrib. Comput., 64(11): 1270--1285, Nov. 2004. ISSN 0743-7315. URL http://pdf.aminer.org/000/297/337/static_and_adaptive_data_replication_algorithms_for_fast_information_access.pdf.
[13]
N. Mohd. Zin, A. Noraziah, A. Che Fauzi, and T. Herawan. Replication techniques in data grid environments. In J.-S. Pan, S.-M. Chen, and N. Nguyen, editors, Intelligent Information and Database Systems, volume 7197 of Lecture Notes in Computer Science, pages 549--559. Springer Berlin Heidelberg, 2012. ISBN 978-3-642-28489-2. URL http://dx.doi.org/10.1007/978-3-642-28490-8_57.
[14]
S. Naseera and K. M. Murthy. Agent based replica placement in a data grid environement. Computational Intelligence, Communication Systems and Networks, International Conference on, 0: 426--430, 2009.
[15]
D. Serrano, M. Patino-Martinez, R. Jimenez-Peris, and B. Kemme. Boosting database replication scalability through partial replication and 1-copy-snapshot-isolation. In Dependable Computing, 2007. PRDC 2007. 13th Pacific Rim International Symposium on, pages 290--297, Dec 2007. URL http://www.researchgate.net/publication/200023090_Boosting_Database_Replication_Scalability_through_Partial_Replication_and_1-Copy-Snapshot-Isolation/links/0deec520a3cdf6504e000000.
[16]
K. Tolle, D. Tansley, and A. Hey. The fourth paradigm: Data-intensive scientific discovery {point of view}. Proceedings of the IEEE, 99(8): 1334--1337, Aug 2011. ISSN 0018-9219. URL http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5958175.
[17]
S. Venugopal, R. Buyya, and K. Ramamohanarao. A taxonomy of data grids for distributed data sharing, management, and processing. ACM Comput. Surv., 38(1), June 2006. ISSN 0360-0300. URL http://www.cloudbus.org/reports/DataGridTaxonomy.pdf.
[18]
Z. Wang, T. Li, N. Xiong, and Y. Pan. A novel dynamic network data replication scheme based on historical access record and proactive deletion. J. Supercomput., 62(1): 227--250, Oct. 2012. ISSN 0920-8542. URL http://dx.doi.org/10.1007/s11227-011-0708-z.
[19]
O. Wolfson. A distributed algorithm for adaptive replication of data. Technical report, Department of Computer Science, Columbia University, 1990. URL http://hdl.handle.net/10022/AC:P:21285.
[20]
O. Wolfson and A. Milo. The multicast policy and its relationship to replicated data placement. ACM Trans. Database Syst., 16(1): 181--205, Mar. 1991. ISSN 0362-5915. URL http://academiccommons.columbia.edu/catalog/ac%3A142996.
[21]
O. Wolfson, S. Jajodia, and Y. Huang. An adaptive data replication algorithm. ACM Trans. Database Syst., 22(2): 255--314, June 1997. ISSN 0362-5915. URL http://www.cs.uic.edu/~wolfson/mobile_ps/tods-adaptive-replication.pdf.
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- Adaptive strength geo-replication strategy
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Published: 21 April 2015
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View all- Naseera S(2017)A survey on data replication strategies in a Data Grid environmentMultiagent and Grid Systems10.3233/MGS-16025312:4(253-269)Online publication date: 13-Jan-2017
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