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A Latency-Aware Multiple Data Replicas Placement Strategy for Fog Computing

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A Correction to this article was published on 19 November 2019

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Abstract

With the rapid increase of the number of IoT devices, transmitting big amount of data from these devices to data centers which are far away will cause problems like high latency or network congestions. Fog Computing provides a better solution for Fog-enabled latency sensitive data services to place data on Fog nodes which are closer to the data generators. However, recent studies only focus on the data placement problem of placing one single data replica to the proper Fog node. Under the situation that there are several data consumers whose topology positions are different subscribing the same data, one single data replica cannot meet the latency requirement of all the consumers. Hence, we build a multi-replica data placement model iFogStorM for Fog Computing to formulate the problem of how many data replicas need to be placed on Fog nodes and how to optimize the data placement. Furthermore, we propose a greedy algorithm based data replica placement strategy, MultiCopyStorage, to reduce the overall latency. MultiCopyStorage uses a pruning method to filter the inferior solutions calculates the overall latency and chooses the solution with the minimum overall latency as the final solution. We conducted experiments on iFogSim, a toolkit for modeling and simulation of Fog Computing, evaluated the proposed strategy with the CloudStorage strategy, Closest Node strategy, iFogStor strategy, and two kinds of heuristic strategy, iFogStorZ, and iFogStorG. The experiment result demonstrates that MultiCopyStorage strategy reduces the overall latency by 6% and 10% compared to iFogStor and iFogStorG strategy respectively. Meanwhile, execution time of the MultiCopyStorage is less than the heuristic strategy, iFogStorG and iFogStorZ, which proves that the proposed strategy can support real-time scheduling.

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Change history

  • 19 November 2019

    The Publisher regrets an error on the printed front cover of the October 2019 issue. The issue numbers were incorrectly listed as Volume 91, Nos. 10-12, October 2019. The correct number should be: "Volume 91, No. 10, October 2019"

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Acknowledgments

This work is supported by National Natural Science Foundation of China (Grant Nos. 61772205, 61872084), Science and Technology Planning Project of Guangdong Province (Grant Nos. 2017B010126002, 2017A010101008, 2017A010101014, 2017B090901061, 2016A010101018 and 2018KJYZ009), Guangzhou Science and Technology Projects (Grant Nos. 201802010010, 201807010052 and 201610010092), Nansha Science and Technology Projects (Grant No. 2017GJ001), Special Funds for the Development of Industry and Information of Guangdong Province (big data demonstrated applications) in 2017, and the young teachers training of Guangdong police officer college(2018QNGG06).

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Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

    Tiansheng Huang & Weiwei Lin

  2. Institute of Software Application Technology, Guangzhou & Chinese Academy of Sciences, Guangzhou, 511458, China

    Yin Li

  3. Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK

    LiGang He

  4. College of Computer Science and Electronic Engineering & National Supercomputing Centre in Changsha, Hunan University, Changsha, 410082, China

    ShaoLiang Peng

  5. School of Computer Science, National University of Defense Technology, Changsha, 410073, China

    ShaoLiang Peng

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  1. Tiansheng Huang
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  2. Weiwei Lin
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Correspondence to Weiwei Lin or Yin Li.

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Huang, T., Lin, W., Li, Y. et al. A Latency-Aware Multiple Data Replicas Placement Strategy for Fog Computing. J Sign Process Syst 91, 1191–1204 (2019). https://doi.org/10.1007/s11265-019-1444-5

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