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GenACO a multi-objective cached data offloading optimization based on genetic algorithm and ant colony optimization.

Mulki Indana Zulfa1,2, Rudy Hartanto1, Adhistya Erna Permanasari1

  • 1Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, Special Region of Yogyakarta, Indonesia.

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Summary
This summary is machine-generated.

Genetic and Ant Colony Optimization (GenACO) enhances data caching for 5G, edge computing, and IoT by optimizing data distribution. This novel framework significantly reduces time consumption while improving cached data solutions.

Keywords:
ACOCached data offloadingGAGenACOOptimization

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Area of Science:

  • Computer Science
  • Network Engineering

Background:

  • Rapid advancements in 5G technology, edge computing, and the Internet of Things (IoT) are driving improvements in data exchange and management.
  • Edge computing faces challenges with massive data requests and limited storage, necessitating efficient data caching and offloading strategies.
  • Optimizing data caching requires addressing constraints such as data priority, storage limitations, and execution time for enhanced user experience.

Purpose of the Study:

  • To introduce a novel framework, Genetic and Ant Colony Optimization (GenACO), for optimizing cached data distribution.
  • To enhance the objective function value in cached data optimization compared to previous research.
  • To improve the reliability of balancing exploration and exploitation in solution finding.

Main Methods:

  • Proposed a novel framework named Genetic and Ant Colony Optimization (GenACO).
  • Implemented a refined solution selection probability mechanism for balanced exploration and exploitation.
  • Utilized two modes, cyclic and non-cyclic, within the GenACO framework.

Main Results:

  • GenACO demonstrated superior performance in cached data optimization.
  • The objective function value was minimized from 0.4374 to 0.4350.
  • Time consumption was reduced by up to 47% compared to previous methods.

Conclusions:

  • GenACO effectively optimizes cached data solutions, improving average solution quality.
  • The framework successfully balances exploration and exploitation for more reliable results.
  • GenACO offers significant improvements in efficiency and performance for data caching in modern network environments.