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Asia-Pacific Services Computing Conference

APSCC 2016: Advances in Services Computing pp 253–265Cite as

Genetic Based Data Placement for Geo-Distributed Data-Intensive Applications in Cloud Computing

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10065))

Abstract

Running data-intensive applications across the geo-distributed data centers in cloud computing needs to address the problem of how to place the data items to the appropriate data centers. The general methods are mainly hash-based which could be understood as random placement intuitively when the query needs distributed data items. In this paper, We propose an genetic based data placement (GBDP) scheme in which a tripartite graph based model is constructed to formulate the data replica placement problem by leveraging the genetic algorithm, and decompose the original problem into two simplified subproblems, which are solved alternately. Through extensive experiments with synthesized and realistic data items, the performance of the proposed scheme is proved validated.

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References

  1. Expósito, R.R., Taboada, G.L., Ramos, S., et al.: Performance evaluation of data-intensive computing applications on a public IaaS cloud. Comput. J. 59(3), 287–307 (2016)

    Article  Google Scholar 

  2. Xu, H., Li, B.: Joint request mapping and response routing for geo-distributed cloud services. Proc. IEEE INFOCOM 12(11), 854–862 (2013)

    Google Scholar 

  3. Le, K., Bilgir, O., Bianchini, R., Martonosi, M., Nguyen, T.: Managing the cost, energy consumption, and carbon footprint of internet services. Adv. Electron. Electron Phys. 38(1), 487–499 (2008)

    Google Scholar 

  4. HDFS Architecture Guide (2008). http://hadoop.apache.org/docs/r1.2.1/hdfsdesign.html

  5. About Replication in Cassandra. http://www.datastax.com/docs/1.0/clusterarchitecture/replication

  6. Kumar, K.A., Quamar, A., Deshpande, A., et al.: SWORD: workload-aware data placement and replica selection for cloud data management systems. VLDB J. 23(6), 845–870 (2014)

    Article  Google Scholar 

  7. Rochman, Y., Levy, H., Brosh, E.: Resource placement and assignment in distributed network topologies. Proc. IEEE INFOCOM 12(11), 1914–1922 (2013)

    Google Scholar 

  8. Jiao, L., Li, J., Du, W., Fu, X.: Multi-objective data placement for multi-cloud socially aware services. In: Proceedings-IEEE INFOCOM, pp. 28–36, April 2014

    Google Scholar 

  9. Agarwal, S., Dunagan, J., Jain, N., Saroiu, S., Wolman, A., Bhogan, H.: Volley: automated data placement for geo-distributed cloud services. In: USENIX NSDI, pp. 17–32 (2010)

    Google Scholar 

  10. Quamar, A., Kumar, K.A., Deshpande, A.: Sword: scalable workload-aware data placement for transactional workloads. In: Proceedings of the 16th International Conference on Extending Database Technology, pp. 430–441 (2013)

    Google Scholar 

  11. Curino, C., Jones, E., Zhang, Y., et al.: Schism: a workload-driven approach to database replication and partitioning. Proc. Vldb Endow. 3(1–2), 48–57 (2010)

    Article  Google Scholar 

  12. Goyal, N., Mittal, P.: Comparative analysis of genetic algorithm, particle swarm optimization and ant colony optimization for TSP. Artif. Intell. Syst. Mach. Learn. 4(4), 202–206 (2012)

    Google Scholar 

  13. Xu, X., Tang, M.: A new grouping genetic algorithm for the MapReduce placement problem in cloud computing. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1601–1608, July 2014

    Google Scholar 

  14. Hunter, J.S.: The exponentially weighted moving average. J. Qual. Technol. 18(4), 203–210 (1986)

    Google Scholar 

  15. Grefenstette, J.J.: Optimization of control parameters for genetic algorithms. IEEE Trans. Syst. Man Cybern. 16(1), 122–128 (1986)

    Article  Google Scholar 

  16. Adamic, L.A., Huberman, B.A.: Zipf’s law and the internet. Glottometrics 3(1), 143–150 (2002)

    Google Scholar 

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Acknowledgments

The authors would like to acknowledge that this work was partially supported by NSERC, CFI and the National Natural Science Foundation of China (Grant No. 61379111, 61602529, 61672537, 61672539, 61402538, 61202342 and 61403424).

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Weifeng Fan, Jun Peng, Xiaoyong Zhang & Zhiwu Huang

Authors
  1. Weifeng Fan
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  2. Jun Peng
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  3. Xiaoyong Zhang
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  4. Zhiwu Huang
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Corresponding author

Correspondence to Jun Peng .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. North China University of Technology, Beijing, China

    Yanbo Han

  3. University of Murcia, Murcia, Spain

    Gregorio Martínez Pérez

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© 2016 Springer International Publishing AG

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Fan, W., Peng, J., Zhang, X., Huang, Z. (2016). Genetic Based Data Placement for Geo-Distributed Data-Intensive Applications in Cloud Computing. In: Wang, G., Han, Y., Martínez Pérez, G. (eds) Advances in Services Computing. APSCC 2016. Lecture Notes in Computer Science(), vol 10065. Springer, Cham. https://doi.org/10.1007/978-3-319-49178-3_20

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  • DOI: https://doi.org/10.1007/978-3-319-49178-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49177-6

  • Online ISBN: 978-3-319-49178-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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