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Resources allocation optimization algorithm based on the comprehensive utility in edge computing applications.

Yanpei Liu1, Yunjing Zhu1, Yanru Bin1

  • 1School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China.

Mathematical Biosciences and Engineering : MBE
|August 9, 2022
PubMed
Summary
This summary is machine-generated.

This study proposes a mobile edge computing resource optimization algorithm. It improves job classification and resource node categorization, enhancing resource utilization and reducing occupancy for better mobile edge computing performance.

Keywords:
edge computingimproved Naive Bayes algorithmresource allocationresource service node classificationweighted bipartite graph

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Area of Science:

  • Computer Science
  • Distributed Computing
  • Mobile Computing

Background:

  • Mobile edge computing (MEC) faces challenges in resource allocation due to limited classifications of resource nodes and low resource utilization.
  • Efficiently managing resources across multiple users and servers in MEC is crucial for performance.

Purpose of the Study:

  • To propose a novel resource optimization algorithm for mobile edge computing environments.
  • To address the limitations of few resource node classifications and low resource utilization in multi-user, multi-server scenarios.

Main Methods:

  • An improved Naive Bayes algorithm is utilized to classify job types by calculating conditional and posterior probabilities.
  • Resource service nodes are classified into CPU-dominant and I/O-dominant based on utilization rates.
  • A comprehensive utility-based resource allocation strategy is employed, considering resource location, task priority, and network transmission cost, using a weighted bipartite graph method.

Main Results:

  • The proposed algorithm effectively classifies job types and resource service nodes.
  • Demonstrates a reduction in resource occupancy rate.
  • Shows a significant improvement in overall resource utilization rate compared to existing methods.

Conclusions:

  • The developed resource optimization algorithm enhances the efficiency of mobile edge computing.
  • Improved classification and allocation strategies lead to better resource management and utilization.