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Latency and energy-aware adaptive service migration in mobile edge computing.

Lina Li1, Junan Lv1, Shuxin Wang1

  • 1School of Computer Science and Technology, Changchun University, Changchun, 130022, China.

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Summary

Mobile Edge Computing (MEC) service migration balances latency and energy use. A new Deep Q-Network model (NPER-D3QN) optimizes these factors for better user experience and lower operational costs.

Keywords:
Adaptive service migrationDeep Q networksEnergy awarenessLatency awarenessMobile edge computing

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Mobile Edge Computing (MEC) enhances user experience by deploying edge servers.
  • Service migration is crucial for maintaining low latency with user mobility.
  • High migration frequency and resource competition increase energy consumption and costs.

Purpose of the Study:

  • To develop a method for adaptive service migration in MEC that optimizes both latency and energy consumption.
  • To address the challenges of dynamic environments, resource competition, and real-time decision-making in MEC service migration.

Main Methods:

  • Modeled service migration as a bi-objective optimization problem (latency and energy consumption).
  • Formulated the problem as a mixed-integer nonlinear programming problem.
  • Designed a Deep Q-Network (DQN) based model, NPER-D3QN, integrating D3QN, Noisy Net, and prioritized experience replay for adaptive migration strategies.

Main Results:

  • The NPER-D3QN model generates adaptive migration strategies for low latency and energy consumption.
  • Simulation experiments show superior performance compared to existing methods in latency and energy efficiency.
  • Demonstrated advantages in dynamic adaptation and multi-objective collaborative optimization.

Conclusions:

  • The proposed delay- and energy-aware adaptive service migration method effectively addresses MEC challenges.
  • NPER-D3QN offers a robust solution for optimizing service migration in dynamic and resource-competitive MEC environments.
  • The method provides significant improvements in both service latency and energy efficiency for MEC providers.