The Journal of Supercomputing

, Volume 75, Issue 11, pp 7209–7243 | Cite as

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

  • Chunlin Li
  • Jianhang Tang
  • Youlong LuoEmail author


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.


Edge computing Replica selection Node service capability Data access 



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.


  1. 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):13–41CrossRefGoogle Scholar
  2. 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):2795–2808CrossRefGoogle Scholar
  3. 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):3229–3242CrossRefGoogle Scholar
  4. 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 1–6Google Scholar
  5. 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 1–5Google Scholar
  6. 6.
    Satyanarayanan M (2017) The Emergence of Edge Computing. Computer 50(1):30–39CrossRefGoogle Scholar
  7. 7.
    Shi W, Dustdar S (2016) The promise of edge computing. Computer 49(5):78–81CrossRefGoogle Scholar
  8. 8.
    Pan J, Mcelhannon J (2017) Future edge cloud and edge computing for internet of things applications. IEEE Intel Things 5(1):439–449CrossRefGoogle Scholar
  9. 9.
    Bhatia M, Sood SK (2018) Internet of things based activity surveillance of defence personnel. J Ambient Intel Hum Comput 9(6):2061–2076CrossRefGoogle Scholar
  10. 10.
    Bhatia M, Sood SK (2017) A comprehensive health assessment framework to facilitate iot-assisted smart workouts: a predictive healthcare perspective. Comput Ind 92–93:50–66CrossRefGoogle Scholar
  11. 11.
    Bhatia M, Sood SK (2018) Exploring temporal analytics in fog-cloud architecture for smart office healthcare. In: Mobile Networks and Applications, pp 1–19CrossRefGoogle Scholar
  12. 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 213–220Google Scholar
  13. 13.
    Li W, Yang Y, Yuan D (2016) Ensuring cloud data reliability with minimum replication by proactive replica checking. IEEE Trans Comput 65(5):1494–1506MathSciNetCrossRefGoogle Scholar
  14. 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 337–342Google Scholar
  15. 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 225–230Google Scholar
  16. 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 213–220Google Scholar
  17. 17.
    Apache JMeter (2018) Accessed 18 Oct 2018
  18. 18.
    Aral A, Ovatman T (2018) A decentralized replica placement algorithm for edge computing. IEEE Trans Netw Serv Manag 15(2):516–529CrossRefGoogle Scholar
  19. 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, USAGoogle Scholar
  20. 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 140–151Google Scholar
  21. 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 786–789Google Scholar
  22. 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 48:140–158CrossRefGoogle Scholar
  23. 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 105–110Google Scholar
  24. 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 5255–5260Google Scholar
  25. 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 143–153Google Scholar
  26. 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 95:249–264CrossRefGoogle Scholar
  27. 27.
    Altiparmak N, Tosun A (2016) Multithreaded maximum flow based optimal replica selection algorithm for heterogeneous storage architectures. IEEE Trans Comput 65(5):1543–1557MathSciNetCrossRefGoogle Scholar
  28. 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):813–829CrossRefGoogle Scholar
  29. 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):55–69CrossRefGoogle Scholar
  30. 30.
    Yanling Shao, Chunlin Li, Hengliang Tang (2019) A data replica placement algorithm for IoT workflows in collaborative edge and cloud environments. Comput Netw 148:46–59CrossRefGoogle Scholar
  31. 31.
    Mansouri N, Dastghaibyfard GH (2012) A dynamic replica management algorithm in data grid. J Netw Comput Appl 35(4):1297–1303CrossRefGoogle Scholar
  32. 32.
    Wu X, Guan H (2016) Data set replica placement algorithm based on fuzzy evaluation in the cloud. J Intell Fuzzy Syst 31(6):2859–2868CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Shaanxi Key Laboratory of Land ConsolidationXi’anPeople’s Republic of China
  2. 2.Department of Computer ScienceWuhan University of TechnologyWuhanPeople’s Republic of China

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