Skip to main content
SpringerLink
Log in

Scalable replica selection based on node service capability for improving data access performance in edge computing environment

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The replica strategies in traditional cloud computing often result in excessive resource consumption and long response time. In the edge cloud environment, if the replica node cannot be managed efficiently, it will cause problems such as low user’s access speed and low system fault tolerance. Therefore, this paper proposed replica creation and selection strategy based on the edge cloud architecture. The dynamic replica creation algorithm based on access heat (DRC-AH) and replica selection algorithms based on node service capability (DRS-NSC) were proposed. The DRC-AH uses data block as replication granularity and Grey Markov chain to dynamically adjust the number of replicas. After the replica is created, when client receives the user’s request, the DRS-NSC selects the best replica node to respond to the user. The experiments show that the proposed algorithms have significant advantages in prediction accuracy, user’s request response time, resource utilization, etc., and improve the performance of the system to a certain extent.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Yang C, Huang Q, Li Z, Kai L, Fei H (2017) Big data and cloud computing: innovation opportunities and challenges. Int J Digit Earth 10(1):13–41

    Article  Google Scholar 

  2. Xu C, Lei J, Li W, Fu X (2016) Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE ACM Trans Netw 24(5):2795–2808

    Article  Google Scholar 

  3. Jonathan A, Ryden M, Oh K, Chandra A, Weissman J (2017) Nebula: distributed edge cloud for data intensive computing. IEEE Trans. Parallel Distrib Syst 28(11):3229–3242

    Article  Google Scholar 

  4. Mostafa N, Ridhawi I, Hamza A (2015) An intelligent dynamic replica selection model within grid systems. In: GCC Conference & Exhibition, Muscat, Oman. IEEE Computer Society, USA, pp 1–6

  5. Madi MK, Tahir HM, Yusof Y, Hassan S (2015) A novel dynamic replica creation mechanism for data grids. In: Game Physics & Mechanics International Conference, Langkawi, Malaysia. IEEE Computer Society, USA, pp 1–5

  6. Satyanarayanan M (2017) The Emergence of Edge Computing. Computer 50(1):30–39

    Article  Google Scholar 

  7. Shi W, Dustdar S (2016) The promise of edge computing. Computer 49(5):78–81

    Article  Google Scholar 

  8. Pan J, Mcelhannon J (2017) Future edge cloud and edge computing for internet of things applications. IEEE Intel Things 5(1):439–449

    Article  Google Scholar 

  9. Bhatia M, Sood SK (2018) Internet of things based activity surveillance of defence personnel. J Ambient Intel Hum Comput 9(6):2061–2076

    Article  Google Scholar 

  10. Bhatia M, Sood SK (2017) A comprehensive health assessment framework to facilitate iot-assisted smart workouts: a predictive healthcare perspective. Comput Ind 92–93:50–66

    Article  Google Scholar 

  11. Bhatia M, Sood SK (2018) Exploring temporal analytics in fog-cloud architecture for smart office healthcare. In: Mobile Networks and Applications, pp 1–19

    Article  Google Scholar 

  12. Zhao YH, Li CL, Li LY, Zhang P (2017) Dynamic replica creation algorithm based on file heat and node load in hybrid cloud. In: 2017 19th International Conference on Advanced Communication Technology (ICACT), Pyongyang, South Korea. IEEE Computer Society, USA, pp 213–220

  13. Li W, Yang Y, Yuan D (2016) Ensuring cloud data reliability with minimum replication by proactive replica checking. IEEE Trans Comput 65(5):1494–1506

    Article  MathSciNet  Google Scholar 

  14. Qu K, Meng L, Yang Y (2016) A dynamic replica algorithm based on Markov model for hadoop distributed file system (HDFS).In: International Conference on Cloud Computing & Intelligence System, Beijing, China. IEEE, New York, pp 337–342

  15. Nivetha NK, Vijayakumar D (2016) Modeling fuzzy based replication algorithm to improve data availability in cloud datacenter. In: International Conference on Computing Technologies & Intelligent Data Engineering, Kovilpatti, India. IEEE Computer Society, USA, pp 225–230

  16. Ouyang X, Garraghan P, Mckee D, Townend P, Xu J (2016) Straggler detection in parallel computing systems through dynamic threshold calculation. In: 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), Crans-Montana, Switzerland. IEEE, New York, pp 213–220

  17. Apache JMeter (2018) http://jmeter.apache.org/. Accessed 18 Oct 2018

  18. Aral A, Ovatman T (2018) A decentralized replica placement algorithm for edge computing. IEEE Trans Netw Serv Manag 15(2):516–529

    Article  Google Scholar 

  19. Rajalakshmi A, Vijayakumar D, Srinivasagan KG (2014) An improved dynamic data replica selection and placement in cloud. In: International Conference on Recent Trends in Information Technology, Chennai, India. IEEE, New York, USA

  20. Zhang B, Wang XW, Huang M (2014) A PGSA based data replica selection scheme for accessing cloud storage system. Advanced Computer Architecture. Springer, Berlin, pp 140–151

    Google Scholar 

  21. Jiang W, Xie H, Zhou X, Fang L, Wang J (2017) Performance analysis and improvement of replica selection algorithms for key-value stores. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), Honolulu, CA, USA. IEEE Computer Society, USA, pp 786–789

  22. Hamrouni T, Slimani S, Charrada FB (2016) A survey of dynamic replication and replica selection strategies based on data mining techniques in data grids. Eng Appl Artif Intel 48:140–158

    Article  Google Scholar 

  23. Navimipour NJ, Milani BA (2016) Replica selection in the cloud environments using an ant colony algorithm. In: Third International Conference on Digital Information Processing, Moscow, Russia. IEEE, New York, pp 105–110

  24. Xue M, Shen J, Guo X (2016) Two phase enhancing replica selection in cloud storage system. In: Control Conference, Chengdu, China. IEEE, New York, pp 5255–5260

  25. Su Y, Feng D, Hua Y, Shi Z, Zhu T (2018) NetRS: cutting response latency in distributed key-value stores with in-network replica selection. In: IEEE International Conference on Distributed Computing Systems, Vienna, Austria. IEEE Computer Society, USA, pp 143–153

  26. Chunlin Li, Tang Jianhang, Hengliang Tang, Luo Youlong (2019) Collaborative cache allocation and task scheduling for data-intensive applications in edge computing. Future Gener Comput Syst 95:249–264

    Article  Google Scholar 

  27. Altiparmak N, Tosun A (2016) Multithreaded maximum flow based optimal replica selection algorithm for heterogeneous storage architectures. IEEE Trans Comput 65(5):1543–1557

    Article  MathSciNet  Google Scholar 

  28. Zeng L, Xu S, Wang Y, Kent KB, Bremner D, Xu C (2017) Toward cost-effective replica placements in cloud storage systems with QoS-awareness. Softw Pract Exp 47(6):813–829

    Article  Google Scholar 

  29. Nguyen PH, Sheu TW, Nguyen PT (2014) Using the combination of gm(1,1) and taylor approximation method to predict the academic achievement of student. SOP Trans Appl Math 1(2):55–69

    Article  Google Scholar 

  30. Yanling Shao, Chunlin Li, Hengliang Tang (2019) A data replica placement algorithm for IoT workflows in collaborative edge and cloud environments. Comput Netw 148:46–59

    Article  Google Scholar 

  31. Mansouri N, Dastghaibyfard GH (2012) A dynamic replica management algorithm in data grid. J Netw Comput Appl 35(4):1297–1303

    Article  Google Scholar 

  32. Wu X, Guan H (2016) Data set replica placement algorithm based on fuzzy evaluation in the cloud. J Intell Fuzzy Syst 31(6):2859–2868

    Article  Google Scholar 

Download references

Acknowledgements

The work was supported by the National Natural Science Foundation (NSF) under Grants (Nos. 61672397, 61873341), Application Foundation Frontier Project of Wuhan (No. 2018010401011290), Fund Project of Shaanxi Key Laboratory of Land Consolidation (No. 2019-ZD01). Any opinions, findings, and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.

Author information

Authors and Affiliations

  1. Shaanxi Key Laboratory of Land Consolidation, Xi’an, 710054, People’s Republic of China

    Chunlin Li

  2. Department of Computer Science, Wuhan University of Technology, Wuhan, 430063, People’s Republic of China

    Chunlin Li, Jianhang Tang & Youlong Luo

Authors
  1. Chunlin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianhang Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Youlong Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Youlong Luo.

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

Li, C., Tang, J. & Luo, Y. Scalable replica selection based on node service capability for improving data access performance in edge computing environment. J Supercomput 75, 7209–7243 (2019). https://doi.org/10.1007/s11227-019-02930-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-019-02930-6

Keywords

Search

Navigation