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Edge computing task scheduling method based on user's social relations: a construction and solution for Smart City

Yun Teng1, Zijia Liu2

  • 1Research Department, Jilin University of Finance and Economics, Changchun, Jilin, China.

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Summary
This summary is machine-generated.

This study introduces a new edge computing task-scheduling model for Smart City Libraries that uses social relationships and device connections. The model reduces delays and energy consumption, enhancing security and reliability.

Keywords:
D2D communicationEdge computingSmart City LibrarySocial relationshipsTask scheduling

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Area of Science:

  • Computer Science
  • Smart City Technologies
  • Edge Computing

Background:

  • Smart City Libraries require robust edge computing for efficient service delivery.
  • Existing task-scheduling models often overlook user social dynamics and device proximity.
  • Integrating social relationships and device connectivity is crucial for optimized edge computing.

Purpose of the Study:

  • To propose a novel task-scheduling model for edge computing in Smart City Libraries.
  • To leverage user social relationships and device-to-device (D2D) communication for improved scheduling.
  • To enhance the security, reliability, and efficiency of edge computing task scheduling.

Main Methods:

  • Constructed a user's social relationship graph using mathematical convolution and Jaccard similarity.
  • Developed an equipment connection graph based on Euclidean distance for D2D communication alignment.
  • Created a task-device bipartite graph by combining social and device connection graphs.
  • Utilized the Kuhn-Munkres (KM) algorithm for task execution cost calculation and edge weight determination.

Main Results:

  • The proposed model demonstrated significant reductions in task execution delays and energy consumption.
  • Compared to other algorithms, the model showed optimal performance, with task execution delay increasing from 23 to 32 as the social threshold score changed from 0.2 to 0.6.
  • The approach effectively improved the security and reliability of edge computing task scheduling.

Conclusions:

  • The novel task-scheduling model enhances edge computing performance in Smart City Library contexts.
  • Leveraging social relationships and device connectivity offers a promising direction for future edge computing research.
  • This work contributes to the advancement of edge computing, paving the way for more efficient and secure smart city infrastructure.