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Minimizing Delay and Power Consumption at the Edge.

Erol Gelenbe1,2,3

  • 1Institute of Theoretical & Applied Informatics, Polish Academy of Sciences (IITiS-PAN), 44-100 Gliwice, Poland.

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|January 25, 2025
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Summary
This summary is machine-generated.

This study introduces a new mathematical method for edge computing task allocation. It provides explicit formulas to minimize latency and energy consumption for IoT and 6G applications.

Keywords:
G-networksanalytical solutionedge computinglatency minimizationreducing energy consumptionsensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Applied Mathematics

Background:

  • Edge computing demands low latency, cost, and power for IoT, smart vehicles, and 6G.
  • Existing methods like non-linear optimization and AI (reinforcement learning) face challenges in guaranteeing optimality and providing explicit allocation formulas.

Purpose of the Study:

  • To develop a mathematically principled method for optimal task allocation in edge computing systems.
  • To explicitly compute the fraction of jobs allocated to servers to minimize average latency and energy consumption.

Main Methods:

  • Developed a mathematical model for a multi-server edge system managed by a task distribution platform.
  • Utilized methods from stochastic processes to derive and solve system equations.
  • Derived explicit, linear-complexity formulas for task allocation fractions.

Main Results:

  • The proposed method explicitly calculates the optimal task allocation fractions.
  • Achieves minimization of average job latency and average energy consumption across edge servers.
  • Offers a low computational cost solution compared to heuristic or simulation-based approaches.

Conclusions:

  • This principled, mathematically based approach provides explicit formulas for optimal task distribution in edge computing.
  • The method overcomes limitations of prior work by guaranteeing optimality and offering computational efficiency.
  • Enables efficient resource management for latency-sensitive and energy-constrained edge applications.