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Optimizing low-power task scheduling for multiple users and servers in mobile edge computing by the MUMS framework.

Guangxu Li1,2,3, Junke Li1,2,3,4

  • 1College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.

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Summary

This study introduces a Multi-User Multi-Server (MUMS) framework for Mobile Edge Computing (MEC) in the Internet of Things (IoT). It significantly reduces energy consumption using the L1_PSO algorithm.

Keywords:
Energy consumption optimizationLow-power task schedulingMobile edge computingParticle swarm optimizationPower consumption modeling

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • The Internet of Things (IoT) faces significant energy consumption challenges.
  • Mobile Edge Computing (MEC) is crucial for addressing IoT energy demands.
  • Efficient task scheduling in MEC is imperative for energy saving.

Purpose of the Study:

  • To propose a Multi-User Multi-Server (MUMS) scheduling framework to reduce energy consumption in MEC systems.
  • To develop an energy consumption optimization model for MUMS.
  • To enhance task scheduling efficiency in IoT environments.

Main Methods:

  • Defined four fundamental models: communication, offloading, energy, and delay for multi-user multi-server systems.
  • Integrated models to construct an energy consumption optimization model for MUMS.
  • Utilized the L1_PSO algorithm, an enhanced Particle Swarm Optimization, to solve the optimization problem.

Main Results:

  • The MUMS framework demonstrated reasonableness and feasibility compared to typical scheduling algorithms.
  • The L1_PSO algorithm achieved a 4.6% reduction in energy consumption versus Random Assignment.
  • The L1_PSO algorithm reduced energy consumption by 2.3% compared to conventional Particle Swarm Optimization.

Conclusions:

  • The proposed MUMS framework effectively reduces energy consumption in MEC for IoT.
  • L1_PSO is a superior optimization algorithm for MEC task scheduling.
  • This research offers a viable solution for energy-efficient IoT deployments.