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Energy Criticality Avoidance-Based Delay Minimization Ant Colony Algorithm for Task Assignment in

Xiaoyao Huang1, Bo Lei1, Guoliang Ji2

  • 1Research Institute China Telecom, Beijing 102209, China.

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|July 14, 2023
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Summary
This summary is machine-generated.

This study optimizes mobile edge computing by balancing task offloading delay and mobile server operational time. The proposed algorithm enhances system quality of service (QoS) for mobile users.

Keywords:
energy use balancingmobile edge computingmobile serverstask assignment

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Mobile edge computing (MEC) is crucial for delay-sensitive services.
  • Mobile servers can assist fixed edge servers with task offloading.
  • Optimizing task assignment and energy management in MEC is challenging.

Purpose of the Study:

  • To minimize system delay in mobile-server-assisted edge computing.
  • To improve overall system quality of service (QoS) by extending mobile server operational time.
  • To address the NP-hard problem of joint optimization for task assignment and energy management.

Main Methods:

  • Formulated the problem as a mixed-integer programming (MIP) problem.
  • Developed a dynamic energy criticality avoidance-based delay minimization ant colony algorithm (EACO).
  • Analyzed the algorithm's computational complexity.

Main Results:

  • The EACO algorithm effectively balances task offloading delay and mobile server operational time.
  • Simulations show superior performance compared to benchmark algorithms.
  • The proposed method enhances system QoS from a long-term perspective.

Conclusions:

  • The EACO algorithm provides an effective solution for joint optimization in mobile-server-assisted MEC.
  • Prolonging mobile server operational time is key to improving long-term system QoS.
  • This research offers a novel approach to delay minimization and energy management in edge computing networks.