A Computational Offloading Research Model based on Particle Swarm Optimization Algorithm in Heterogeneous Networks
Abstract
Computing offl oading refers to offl oading computing tasks from the core side of the network to edge devices close to users for
processing, to reduce latency and enhance user experience. This paper proposes a computational offl oading scheme based on a particle optimization algorithm to effectively reduce the economic cost of user computational offl oading in an ultra-dense heterogeneous network with
multiple users and single tasks. The particle swarm optimization algorithm refers to fi nding the optimal solution by simulating the behavior of
a fl ock of birds, each particle searches for the optimal solution separately in the search space, and the particles share information with other
particles in the swarm to fi nd the optimal individual extreme value as the global optimal solution of the entire particle swarm.
processing, to reduce latency and enhance user experience. This paper proposes a computational offl oading scheme based on a particle optimization algorithm to effectively reduce the economic cost of user computational offl oading in an ultra-dense heterogeneous network with
multiple users and single tasks. The particle swarm optimization algorithm refers to fi nding the optimal solution by simulating the behavior of
a fl ock of birds, each particle searches for the optimal solution separately in the search space, and the particles share information with other
particles in the swarm to fi nd the optimal individual extreme value as the global optimal solution of the entire particle swarm.
Keywords
Computational offl oading; Particle swarm optimization; Ultra-dense heterogeneous networks
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PDFReferences
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DOI: http://dx.doi.org/10.70711/aitr.v2i7.5980
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