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Latency-Aware Deployment of IoT Services in a Cloud-Edge Environment

  • Shouli ZhangEmail author
  • Chen Liu
  • Jianwu Wang
  • Zhongguo Yang
  • Yanbo Han
  • Xiaohong Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11895)

Abstract

Efficient scheduling of data elements and computation units can help to reduce the latency of processing big IoT stream data. In many cases, moving computation turns out to be more cost-effective than moving data. However, deploying computations from cloud-end to edge devices may face two difficult situations. First, edge devices usually have limited computing power as well as storage capability, and we need to selectively schedule computation tasks. Secondly, the overhead of stream data processing varies over time and makes it necessary to adaptively adjust service deployment at runtime. In this work, we propose a heuristics approach to adaptively deploying services at runtime. The effectiveness of the proposed approach is demonstrated by examining real cases of China’s State Power Grid.

Keywords

Big IoT stream processing Edge computing Data overhead Adaptive service deployment 

Notes

Acknowledgement

This work is supported by “National Natural Science Foundation of China (No:61672042), Models and Methodology of Data Services Facilitating Dynamic Correlation of Big Stream Data”, “National Natural Science Foundation of China (No.61702014)”, and “Beijing Natural Science Foundation (No. 4192020)”.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shouli Zhang
    • 1
    • 2
    • 3
    Email author
  • Chen Liu
    • 2
    • 3
  • Jianwu Wang
    • 4
  • Zhongguo Yang
    • 2
    • 3
  • Yanbo Han
    • 1
    • 2
    • 3
  • Xiaohong Li
    • 1
  1. 1.Division of Intelligence and ComputingTianjin UniversityTianjinChina
  2. 2.Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream DataNorth China University of TechnologyBeijingChina
  3. 3.Cloud Research CenterNorth China University of TechnologyBeijingChina
  4. 4.Department of Information SystemsUniversity of MarylandBaltimore County, BaltimoreUSA

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