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Resource utilization and cost optimization oriented container placement for edge computing in industrial internet

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Abstract

With the continuous evolution of the modern industrial internet in an intelligent direction, intelligent devices generate many delay-sensitive task requests. Container-based edge computing service deployment can save the bandwidth resources of the core network and reduce service delay. However, an unreasonable container deployment leads to the waste of edge server resources and fails to meet the requirements of real-time processing of services. In this paper, we establish a basic container deployment model to optimize the resource utilization and deployment cost. On this basis, we additionally establish a fault-tolerant deployment model for containers, which enables the edge computing system still provide services and improves the fault-tolerant ability of the factory when the container deployment fails or deployment speed drops caused by hardware failures. To solve the optimal deployment strategy, we propose an improved genetic-simulated annealing algorithm (IGSAA). The proposed algorithm can achieve optimal container deployment by improving initialization, crossover and mutation operations of the genetic algorithm. The simulation results show that the established model has remarkable effect in resource utilization and cost optimization. Compared with the existing deployment algorithms, IGSAA outperforms them by at least 22% in optimizing resource utilization and deployment costs.

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Abbreviations

EC:

Edge computing

MCC:

Mobile cloud computing

MECO:

Mobile edge cloud offload

IoT:

Internet of things

ECHO:

Edge cloud heuristic

VR:

Virtual reality

IGSAA:

Improved genetic-simulated annealing algorithm

DQN:

Deep Q-network

GA:

Genetic algorithm

SA:

Simulated annealing algorithm

HC:

Hill climbing algorithm

GWO:

Grey wolf optimizer

WOA:

Whale optimization algorithm

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Acknowledgements

This work was supported by the Communication Soft Science Program of Ministry of Industry and Information Technology of China (No. 2022-R-43), the Natural Science Basic Research Program of Shaanxi (No. 2021JQ-719), the Graduate Innovation Fund of Xi’an University of Posts and Telecommunications (No. CXJJYL2021021), the Youth Innovation Team of Shaanxi Universities “Industial Big Data Analysis and Intelligent Processing”, and the Special Funds for Construction of Key Disciplines in Universities in Shaanxi.

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Authors and Affiliations

  1. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, 710121, Shaanxi, China

    Yanping Chen, Shengsheng He, Xiaomin Jin & Zhongmin Wang

  2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts and Telecommunications, Xi’an, 710121, Shaanxi, China

    Yanping Chen, Shengsheng He, Xiaomin Jin & Zhongmin Wang

  3. Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an, 710121, Shaanxi, China

    Yanping Chen, Shengsheng He, Xiaomin Jin & Zhongmin Wang

  4. ZTE Corporation, No.55, Hi-tech Road South, Shenzhen, 51805, China

    Fengwei Wang & Lei Chen

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  1. Yanping Chen
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  2. Shengsheng He
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  3. Xiaomin Jin
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Correspondence to Shengsheng He.

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Chen, Y., He, S., Jin, X. et al. Resource utilization and cost optimization oriented container placement for edge computing in industrial internet. J Supercomput 79, 3821–3849 (2023). https://doi.org/10.1007/s11227-022-04801-z

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