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GP4ESP: a hybrid genetic algorithm and particle swarm optimization algorithm for edge server placement.

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Summary
This summary is machine-generated.

This study introduces GP4ESP, a hybrid meta-heuristic algorithm for edge server placement (ESP). GP4ESP significantly reduces overall response time by combining genetic algorithms and particle swarm optimization for efficient edge computing.

Keywords:
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Area of Science:

  • Computer Science
  • Distributed Systems
  • Artificial Intelligence

Background:

  • Edge computing offers ultra-low latency services crucial for smart devices and intelligent applications.
  • Edge server placement (ESP) is vital for efficient request processing but is an NP-hard problem.
  • Existing meta-heuristic algorithms for ESP have limitations due to single-strategy exploitation or ignoring computing delays.

Purpose of the Study:

  • To formulate the ESP problem with the objective of minimizing overall response time, considering heterogeneous edge servers.
  • To propose an effective hybrid meta-heuristic algorithm for solving the ESP problem.
  • To evaluate the performance of the proposed algorithm against existing methods.

Main Methods:

  • Formulated the Edge Server Placement (ESP) problem considering heterogeneous edge servers and minimizing overall response time.
  • Developed a hybrid meta-heuristic algorithm, GP4ESP, integrating Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).
  • Conducted extensive simulation experiments to evaluate GP4ESP's performance.

Main Results:

  • GP4ESP achieved 18.2%-20.7% shorter overall response time compared to eleven state-of-the-art ESP algorithms.
  • The performance improvement of GP4ESP was consistent across varying scales of the ESP problem.
  • The hybrid approach effectively fused the global search of GA with the fast convergence of PSO.

Conclusions:

  • GP4ESP offers a superior solution for the Edge Server Placement problem, outperforming existing algorithms.
  • The hybrid meta-heuristic approach effectively addresses the limitations of single-strategy algorithms and computing delay considerations.
  • This work contributes to more efficient and effective edge computing resource management.