Skip to main content
SpringerLink
Log in

Load Balancing Algorithms for Big Data Flow Classification Based on Heterogeneous Computing in Software Definition Networks

  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

Abstract

Distributed network architecture of heterogeneous computing faces with such problems as strict performance constraints of network control, unpredictable mapping relationship between computing data algorithms of different mobile terminals and inconsistency between computing algorithms and link control of data networks. In order to solve the above problems, we begin with software definition network architecture and load balancing algorithm for heterogeneous computing, and gradually improve the real-time and reliability of heterogeneous computing. On the one hand, the heterogeneous computing data of fog node and cloud computing system are distributed. The centralized service of software-defined network combines with distributed computing of mobile edge terminal and its subnet. On the other hand, we define the centralized information and distributed scheduler of the network. In addition, we deploy the optimal assignment of data sharing and heterogeneous computing tasks in real time with ellipse-partitioned area as the object. A series of algorithms for classifying and assigning heterogeneous computing data streams in software-defined networks are designed to achieve the optimal balance among load balancing, minimum classification of large data streams, minimum resource occupation and time constraints. Experimental comparison compared and evaluated the Load Balancing with big data stream (LBBS), Load Balancing with Heterogeneous Computing (LBHC) and the proposed LBBHD. Compared with the other two algorithms, the proposed algorithm improves workload skewness, throughput and load balancing error respectively about 2.1%, 1.96%, 2.9%, 2.2%; 5.57%. 2.51%.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Yan J, Jin D. VT-Mininet: Virtual-Time-Enabled Mininet for Scalable and Accurate Software-Define Network Emulation[J]. 2015

  2. Qing-Yun, Z., Ming, C., Guang-Song, Z., et al.: Research on OpenFlow-based SDN technologies[J]. J. Softw. 24(5), 1078–1097 (2013)

    Article  Google Scholar 

  3. Dixit, K.: Ramana, et al. towards an elastic distributed SDN controller[J]. Comput. Commun. Rev. 43(4), 7–12 (2013)

    Article  Google Scholar 

  4. Zaharia, M., Xin, R.S., Wendell, P., et al.: Apache spark: a unified engine for big data processing[J]. Commun. ACM. 59(11), 56–65 (2016)

    Article  Google Scholar 

  5. Kim, H.S., Kim, H., Paek, J., et al.: Load balancing under heavy traffic in RPL routing protocol for low power and Lossy networks[J]. IEEE Trans. Mob. Comput. 16(4), 964–979 (2017)

    Article  Google Scholar 

  6. Tang F, Li L, Barolli L, et al. An Efficient Sampling and Classification Approach for Flow Detection in SDN-Based Big Data Centers[C]// 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA). IEEE Computer Society, 2017

  7. del Río, S., López, V., Benítez, J.M., et al.: A map reduce approach to address big data classification problems based on the fusion of linguistic fuzzy rules[J]. Int. J. Comp. Intell. Syst. 8(3), 422–437 (2015)

    Article  Google Scholar 

  8. Koleva, P., Poulkov, V., Asenov, O.: Resource management based on dynamic users Association for Future Heterogeneous Telecommunication Access Infrastructures[J]. Wirel. Pers. Commun. 78(3), 1595–1611 (2014)

    Article  Google Scholar 

  9. Braun, T.D., Siegel, H.J., Maciejewski, A.A., et al.: Static resource allocation for heterogeneous computing environments with tasks having dependencies, priorities, deadlines, and multiple versions[J]. J. Parallel and Distributed Computing. 68(11), 1504–1516 (2008)

    Article  Google Scholar 

  10. Badshah, J., Kamran, M., Shah, N., et al.: An improved method to deploy cache servers in software defined network-based information centric networking for big data[J]. Journal of Grid Computing. 17, 255–277 (2019)

    Article  Google Scholar 

  11. Kim, B.-S., Aldwairi, M., Kim, K.-I.: An efficient real-time data dissemination multicast protocol for big data in wireless sensor networks[J]. Journal of Grid Computing. 17, 341–355 (2019)

    Article  Google Scholar 

  12. Liu, J., Pacitti, E., Valduriez, P., et al.: A survey of data-intensive scientific workflow management[J]. Journal of Grid Computing. 13, 457–493 (2015)

    Article  Google Scholar 

  13. Zhang Y, Deng L, Chen M et al. Joint bidding and geographical load balancing for datacenters: is uncertainty a blessing or a curse?[J]. IEEE/ACM Trans. Networking, 2018:1–14

  14. Wan, J., Chen, B., Wang, S., et al.: Fog computing for energy-aware load balancing and scheduling in smart factory[J]. IEEE Transactions on Industrial Informatics. 1–1 (2018)

  15. Lin, W., Peng, G., Bian, X., et al.: Scheduling algorithms for heterogeneous cloud environment: Main resource load balancing algorithm and time balancing algorithm[J]. Journal of Grid Computing. 17(4), 699–726 (2019)

    Article  Google Scholar 

  16. Sharifi, L., Cerdà-Alabern, L., Freitag, F., et al.: Energy efficient cloud service provisioning: keeping data center granularity in perspective[J]. Journal of Grid Computing. 14, 299–325 (2016)

    Article  Google Scholar 

  17. Luo, J., Wu, M., Gopukumar, D., et al.: Big Data Application in Biomedical Research and Health Care: A Literature Review[J]. Biomed. Inform. Insights. 8, BII.S31559 (2016)

    Article  Google Scholar 

  18. Bossaerts, P., Murawski, C.: Computational complexity and human decision-making[J]. Trends Cogn. Sci. 21(12), 917–929 (2017)

    Article  Google Scholar 

  19. Doğan, A., Özgüner, F.: Biobjective scheduling algorithms for execution time–reliability trade-off in heterogeneous computing systems[J]. Comput. J. 48(3), 300–314 (2018)

    Article  Google Scholar 

  20. Alebrahim, S., Ahmad, I.: Task scheduling for heterogeneous computing systems[J]. J. Supercomput. 73(6), 2313–2338 (2017)

    Article  Google Scholar 

  21. Meyerhenke H, Sanders P, Schulz C. Parallel graph partitioning for complex networks[J]. IEEE Transactions on Parallel and Distributed Systems, 2017:1–1

Download references

Author information

Authors and Affiliations

  1. Shenzhen Water Planning & Design Institute Co., Ltd., Shenzhen, 518001, China

    Yang Ping

Authors
  1. Yang Ping
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yang Ping.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ping, Y. Load Balancing Algorithms for Big Data Flow Classification Based on Heterogeneous Computing in Software Definition Networks. J Grid Computing 18, 275–291 (2020). https://doi.org/10.1007/s10723-020-09511-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10723-020-09511-5

Keywords

Search

Navigation