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Fog-Edge Collaborative Task Offloading Strategy Based on Chaotic Teaching and Learning Particle Swarm Optimization.

Songyue Han1,2, Wei Huang1, DaWei Ma1

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Summary
This summary is machine-generated.

This study introduces a cloud-fog-edge-end architecture and a novel chaos teaching particle swarm optimization (CTLPSO) algorithm to enhance mobile edge computing (MEC) resource allocation for smart campuses. The CTLPSO algorithm improves task offloading success rates and reduces system overhead.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Mobile Edge Computing (MEC) faces challenges balancing high business demand with limited resources.
  • Smart campus environments require efficient resource management for diverse applications.

Purpose of the Study:

  • To develop a collaborative architecture and optimization model for MEC resource allocation.
  • To propose an advanced optimization algorithm for improved task offloading and reduced overhead.

Main Methods:

  • Constructed a "cloud, fog, edge, and end" collaborative architecture for smart campuses.
  • Developed an optimization model for joint computation offloading and resource allocation.
  • Proposed the Chaos Teaching Particle Swarm Optimization (CTLPSO) algorithm, integrating chaos theory and adaptive mechanisms with TLBO.

Main Results:

  • The proposed architecture effectively alleviates limited MEC resources.
  • CTLPSO demonstrates significant advantages in convergence, stability, and complexity compared to existing algorithms.
  • Optimization strategy improves offloading success rate and reduces total system overhead, especially with over 50 tasks.

Conclusions:

  • The developed architecture and CTLPSO algorithm offer a robust solution for MEC resource management in smart campus scenarios.
  • The CTLPSO algorithm provides a superior approach for optimizing task offloading and resource allocation, enhancing system performance.