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Research on key technologies of technological service and management based on cluster load balancing

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Abstract

Detection of similarity in scientific and technical data finds repetition rate of contents in project notification form for technology plan submitted by each unit. It is conducted through comparison between the same batch and previous batches on declaration book from each declaration unit. Furthermore, it affords early warning, which is an important link in normativeness for project management. Usually, cluster system based on Web servers is adopted to reduce data processing time, where loading balancing of each node is a research hotspot. This paper proposed auto-adaptive load balancing (AALB) algorithm to accomplish load balancing for each node in the cluster by predicting request arrival rate and request size and rapidly adjusting parameter value. The experiment proves that data block distribution is more balanced in each node using balancing strategy for AALB, with less time compared with other load balancing.

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References

  1. Wang, C.J.: Research and analysis of LUS cluster algorithm. Comput. Knowl. Technol. 12(3), 456–464 (2013)

    Google Scholar 

  2. Liu, F., Li, H., Yang, R., et al.: Immune stability maintenance inspired model of Web services load balance. In: 2013 Ninth International Conference on Natural Computation (ICNC), pp. 649–653. IEEE, New York (2013)

  3. Chi, X., Liu, B., Niu, Q., et al.: Web load balance and cache optimization design based Nginx under high-concunrency environment. In: 2012 Third International Conference on Digital Manufacturing & Automation, pp. 1029–1032. IEEE Computer Society, Washington, DC (2012)

  4. Liu, Y.Y., Shen, M.Y.: Improved WLC scheduling algorithm of Linux Virtual Server load balance. Manuf. Autom. 32(9), 187–191 (2010)

    Google Scholar 

  5. Yang, J., Zhang, D.: Content-based dynamic load-balancing algorithm of web server. Comput. Eng. 36(13), 82–86 (2010)

    MathSciNet  Google Scholar 

  6. Ren, X., Lin, R., Zou, H.: A dynamic load balancing strategy for cloud computing platform based on exponential smoothing forecast. In: 2011 IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 220–224. IEEE, Beijing (2011)

  7. Varone, L., Gratani, L.: Leaf respiration responsiveness to induced water stress in Mediterranean species. Environ. Exp. Bot. 109, 141–150 (2015)

    Article  Google Scholar 

  8. Gringeri, S., Bitar, N., Xia, T.J.: Extending software defined network principles to include optical transport. IEEE Commun. Mag. 51(3), 32–40 (2013)

    Article  Google Scholar 

  9. Cardellini, V., Colajanni, M., Yu, P.S.: DNS dispatching algorithms with state estimators for scalable Web-server clusters. World Wide Web 2(3), 101–113 (1999)

    Article  Google Scholar 

  10. Colajanni, M., Yu, P.S., Dias, D.M.: Analysis of task assignment policies in scalable distributed Web-server systems. IEEE Trans. Parallel Distrib. Syst. 9(6), 585–600 (1998)

    Article  Google Scholar 

  11. Zhou, Qingyuan, Luo, Jianjian: The study on evaluation method of urban network security in the big data era. Intell. Autom. Soft Comput. (2017). doi:10.1080/10798587.2016.1267444

    Google Scholar 

  12. Zhou, Qingyuan, Luo, Jianjian: Artificial neural network based grid computing of E-government scheduling for emergency management. Comput. Syst. Sci. Eng. 30(5), 327–335 (2015)

    Google Scholar 

  13. Zhou, Qingyuan, Luo, Juan: The service quality evaluation of ecologic economy systems using simulation computing. Comput. Syst. Sci. Eng. 31(6), 453–460 (2016)

    MathSciNet  Google Scholar 

  14. Zhou, Qingyuan, Luo, Jianjian: The risk management using limit theory of statistics on extremes on the big data era. J. Comput. Theor. Nanosci. 12, 6237–6243 (2015). doi:10.1166/jctn.2015.4661

    Article  Google Scholar 

  15. Duan, Z., Gu, Z.: Dynamic load balancing in web cache cluster. In: Seventh International Conference on Grid and Cooperative Computing (GCC’08), vol. 2008, pp. 147–150. IEEE, Washington, DC (2008)

  16. Puccinelli, D., Haenggi, M.: Lifetime benefits through load balancing in homogeneous sensor networks. In: Wireless Communications and Networking Conference (WCNC 2009), pp. 1–6. IEEE, Washington (2009)

  17. Riska, A., Sun, W., Smirni, E., et al.: ADAPTLOAD: effective balancing in clustered Web servers under transient load conditions. In: Proceedings of the 22nd International Conference on Distributed Computing Systems, 2002, pp. 104–111. IEEE, Los Alamitos (2002)

  18. Zhang, Q., Riska, A., Sun, W., et al.: Workload-aware load balancing for clustered web servers. IEEE Trans. Parallel Distrib. Syst. 16(3), 219–233 (2005)

    Article  Google Scholar 

  19. Karaoglu, B., Heinzelman, W.: Cooperative load balancing and dynamic channel allocation for cluster-based mobile ad hoc networks. IEEE Trans. Mob. Comput. 14(5), 951–963 (2015)

    Article  Google Scholar 

  20. Dave, A., Patel, B., Bhatt, G.: Load balancing in cloud computing using optimization techniques: a study. In: International Conference on Communication and Electronics Systems (ICCES), pp. 1–6. IEEE, New York (2016)

  21. Wang, S., Zhou, H.: The research of mapreduce load balancing based on multiple partition algorithm. In: 2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC), pp. 339–342. IEEE, Washington, DC (2016)

  22. Kapoor, S., Dabas, C.: Cluster based load balancing in cloud computing. In: 2015 Eighth International Conference on Contemporary Computing (IC3), pp. 76–81. IEEE, New York (2015)

  23. Gutierrez-Garcia, J.O., Ramirez-Nafarrate, A.: Agent-based load balancing in cloud data centers. Cluster Comput. 18(3), 1041–1062 (2015). doi:10.1007/s10586-015-0460-x

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Business Administration, China University of Petroleum, Beijing, China

    Jinhong Xu & Xiaoguang Yang

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  1. Jinhong Xu
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  2. Xiaoguang Yang
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Correspondence to Jinhong Xu.

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Xu, J., Yang, X. Research on key technologies of technological service and management based on cluster load balancing. Cluster Comput 20, 3409–3415 (2017). https://doi.org/10.1007/s10586-017-1103-1

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  • DOI: https://doi.org/10.1007/s10586-017-1103-1

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