A load-adapative cloud resource scheduling model based on ant colony algorithm

2011 ◽  
Author(s):  
Xin Lu ◽  
Zilong Gu
Keyword(s):  
Ant Colony Algorithm ◽  
Resource Scheduling ◽  
Ant Colony ◽  
Model Based ◽  
Scheduling Model ◽  
Cloud Resource ◽  
Cloud Resource Scheduling

Adaptive Cloud Resource Scheduling Model Based on Improved Ant Colony Algorithm

2020 ◽  
Vol 57 (1) ◽  
pp. 010603
Author(s):  
聂清彬 Nie Qingbin ◽  
潘峰 Pan Feng ◽  
吴嘉诚 Wu Jiacheng ◽  
曹耀钦 Cao Yaoqin
Keyword(s):  
Ant Colony Algorithm ◽  
Resource Scheduling ◽  
Ant Colony ◽  
Model Based ◽  
Scheduling Model ◽  
Cloud Resource ◽  
Cloud Resource Scheduling ◽  
Improved Ant Colony Algorithm

scholarly journals Design of Intelligent Voice Acquisition System Based on Cloud Resource Scheduling Model

2022 ◽  
Vol 16 ◽  
pp. 360-366
Author(s):  
Zhiwu Cui ◽  
Ke Zhou ◽  
Jian Chen
Keyword(s):  
Resource Scheduling ◽  
Transmission Function ◽  
Acquisition System ◽  
Communication Link ◽  
Data Set ◽  
Access Port ◽  
Scheduling Model ◽  
Cloud Resource ◽  
Voice Data ◽  
Cloud Resource Scheduling

The existing acquisition system has the problem of imperfect communication link, which leads to the weak signal receiving strength of the system. This paper designs an intelligent voice acquisition system based on cloud resource scheduling model. Hardware: select S3C6410 as hardware platform, optimize audio access port, connect IIS serial bus and other components; Software part: extract the frequency agility characteristics of intelligent voice signal, predict the future sample value, establish the communication link with cloud resource scheduling model, obtain the communication rate information, code and generate digital voice data, set the transmission function of intelligent acquisition system with overlay algorithm. Experimental results: the average signal receiving strength of the designed system and the other two intelligent voice intelligent acquisition systems is 106.40 dBm, 91.33 dBm and 90.23 dBm, which proves that the intelligent acquisition system integrated with cloud resource scheduling model has higher use value.

Simulation of English text recognition model based on ant colony algorithm and genetic algorithm

2021 ◽  
pp. 1-12
Author(s):  
Fei Long
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Text Recognition ◽  
English Text ◽  
Recognition Model ◽  
Central Node ◽  
Model Based ◽  
Characteristic Points

The difficulty of English text recognition lies in fuzzy image text classification and part-of-speech classification. Traditional models have a high error rate in English text recognition. In order to improve the effect of English text recognition, guided by machine learning ideas, this paper combines ant colony algorithm and genetic algorithm to construct an English text recognition model based on machine learning. Moreover, based on the characteristics of ant colony intelligent algorithm optimization, a method of using ant colony algorithm to solve the central node is proposed. In addition, this paper uses the ant colony algorithm to obtain the characteristic points in the study area and determine a reasonable number, and then combine the uniform grid to select some non-characteristic points as the central node of the core function, and finally use the central node with a reasonable distribution for modeling. Finally, this paper designs experiments to verify the performance of the model constructed in this paper and combines mathematical statistics to visually display the experimental results using tables and graphs. The research results show that the performance of the model constructed in this paper is good.

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