Skip to main content
SpringerLink
Log in
Book cover

International Congress on High-Performance Computing and Big Data Analysis

TopHPC 2019: High-Performance Computing and Big Data Analysis pp 143–155Cite as

Analytical Comparison of Virtual Machine and Data Placement Algorithms for Big Data Applications Based on Cloud Computing

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 891))

Abstract

Data generation proliferation and big data utilization increase in different areas, cause to highlight data and virtual machine placement problem in MapReduce framework. Since problem NP-hardness is proven, different algorithms using various techniques have been proposed in recent years and every solution targets some objectives in this regard. But, it has not proposed any troubleshooting solution to placement problem till now and it is still an issue for service providers. In this paper, we present a comprehensive evaluation of current researches and highlight the new paths for researchers by identifying the weaknesses of existing studies. To reach to this goal, we evaluate many of current researches about vm placement in cloud computing and big data applications on cloud computing and select some of them to be presented in this paper. Also, we propose our method to solve the problem and show how it could be more effective than available methods.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Hadoop Distributed File System.

  2. 2.

    Scheduling a task close to the corresponding data is known as data locality [5].

  3. 3.

    Quality of Service.

  4. 4.

    A k-club of a graph G is defined as a maximal subgraph of G of diameter k.

  5. 5.

    It should be noted this evidence is based on the researchers’ experiences in XaaS Cloud implementation. XaaS is the main cloud computing service provider in Iran. Ref: www.XaaS.ir.

References

  1. Hall, L., Harris, B., Tomes, E., Altiparmak, N.: Big data aware virtual machine placement in cloud data centers. In: BDCAT 2017, Austin, Texas, USA, 5–8 December 2017 (2017)

    Google Scholar 

  2. Cisco Global Cloud Index: Forecast and methodology, 2016–2021, Cisco Systems

    Google Scholar 

  3. Yang, S.-J., Chen, Y.-R.: Design adaptive task allocation scheduler to improve MapReduce performance in heterogeneous clouds. J. Netw. Comput. Appl. 57, 61–70 (2015)

    Article  Google Scholar 

  4. Sakr, S., Liu, A., Fayoumi, A.G.: The family of MapReduce and large scale data processing systems. ACM Comput. Surv. (CSUR) 46(1) (2013). Article no. 11

    Article  Google Scholar 

  5. Xu, H., Liu, W., Shu, G., Li, J.: LDBAS: location-aware data block allocation strategy for HDFS-based applications in the cloud. KSII Trans. Internet Inf. Syst. 12(1), 204–226 (2018)

    Google Scholar 

  6. Usmani, Z., Singh, S.: A survey of virtual machine placement techniques in a cloud data center. In: International Conference on Information Security & Privacy (ICISP 2015), Nagpur, India, 11–12 December 2015 (2015)

    Google Scholar 

  7. Attaoui, W., Sabir, E.: Multi-criteria virtual machine placement in cloud computing environments: a literature review. Cornell University, Computer Science, Networking and Internet Architecture (2018)

    Google Scholar 

  8. Palanisamy, B., Singh, A., Liu, L., Jain, B.: Purlieus: locality-aware resource allocation for MapReduce in a cloud. In: SC ‘11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, WA, USA, 12–18 November 2011 (2011)

    Google Scholar 

  9. Li, M., Subhraveti, D., Butt, A.R., Khasymski, A., Sarkar, P.: CAM: a topology aware minimum cost flow based resource manager for MapReduce applications in the cloud. In: HPDC 2012, Delft, The Netherlands, 18–22 June 2012 (2012)

    Google Scholar 

  10. Kuo, J.-J., Yang, H.-H., Tsai, M.-J.: Optimal approximation algorithm of virtual machine placement for data latency minimization in cloud systems. In: IEEE INFOCOM 2014 - IEEE Conference on Computer Communications (2014)

    Google Scholar 

  11. Shabeera, T.P., Kumar, S.D.M., Salam, S.M., Krishnan, K.M.: Optimizing VM allocation and data placement for data-intensive applications in cloud using ACO metaheuristic algorithm. Eng. Sci. Technol. Int. J. 20, 616–628 (2017)

    Article  Google Scholar 

  12. Guerrero, C., Lera, I., Bermejo, B., Juiz, C.: Multi-objective optimization for virtual machine allocation and replica placement in virtualized hadoop. IEEE Trans. Parallel Distrib. Syst. 29(11), 2568–2581 (2018)

    Article  Google Scholar 

  13. Guzek, M., Bouvry, P., Talbi, E.-G.: A survey of evolutionary computation for resource management of processing in cloud computing. IEEE Comput. Intell. Mag. 10(2), 53–67 (2015)

    Article  Google Scholar 

  14. Zhang, J., Huang, H., Wang, X.: Resource provision algorithms in cloud computing: a survey. J. Netw. Comput. Appl. 64, 23–42 (2016)

    Article  Google Scholar 

  15. Mann, Z.A.: Allocation of virtual machines in cloud data centers – a survey of problem models and optimization algorithms. ACM Comput. Surv. 48(1) (2015)

    Article  Google Scholar 

  16. Mann, Z.A., Szabo, M.: Which is the best algorithm for virtual machine placement optimization? Concurr. Comput. Pract. Exp. 29, e4083 (2017)

    Article  Google Scholar 

  17. Li, X.: An energy aware green spine switch management system in spine-leaf datacenter networks. A thesis for the degree of Master of Applied Science in Electrical and Computer, Carleton University (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of New Sciences and Technologies, Tehran University, Tehran, Iran

    Seyyed Mohsen Seyyedsalehi & Mohammad Khansari

Authors
  1. Seyyed Mohsen Seyyedsalehi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Khansari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Khansari .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Lucio Grandinetti

  2. Kharazmi University, Tehran, Iran

    Seyedeh Leili Mirtaheri

  3. University of Calabria, Rende, Italy

    Reza Shahbazian

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Seyyedsalehi, S.M., Khansari, M. (2019). Analytical Comparison of Virtual Machine and Data Placement Algorithms for Big Data Applications Based on Cloud Computing. In: Grandinetti, L., Mirtaheri, S., Shahbazian, R. (eds) High-Performance Computing and Big Data Analysis. TopHPC 2019. Communications in Computer and Information Science, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-33495-6_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33495-6_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33494-9

  • Online ISBN: 978-3-030-33495-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation