Optimization and Implementation of the Spectrum Management

and Optimization Algorithm in the Distributed Computing Platform

Ruoliang Ding

, Chao Liang

and Yan 'an Zhao

2,*

CSSC Systems Engineering Research Institute 102600, China

The State Radio Monitoring Center, Shaanxi Monitoring Station 710299, China

Keywords: Spectrum Information, Distributed Computing Platform, Distributed Computing Platform, Spectrum

Management and Optimization Algorithm.

Abstract: With the wide application of distributed computing platform, the application of spectrum management and

optimization algorithm in distributed computing platform has also been paid attention to. Therefore, this paper

carries out in-depth research on the application of spectrum management and optimization algorithm in

distributed computing platform. First of all, the spectrum management and optimization algorithm is

introduced, and the initial model of spectrum management features and spectrum management features of the

distributed computing platform samples are extracted to realize the application analysis of spectrum

management and optimization algorithm in the distributed computing platform. Then, the spectrum

management and optimization algorithm is used to design the distributed computing platform, build the

distributed computing platform of spectrum management and optimization algorithm, and complete the

application of spectrum management and optimization algorithm in the distributed computing platform. The

simulation experiment results show that the application accuracy of the proposed algorithm is significantly

improved, and the stability of the application effect is better, which can effectively solve the problems of low

accuracy and low practice rate in the current application, and has certain practical value.

1 INTRODUCTION

With the promotion and use of distributed computing

platforms, the executors have higher and higher

requirements for distributed computing

platforms(Ejarque, Domínguez, et al. 2019),

(George, Raghavan, et al. 2005), but it will also lead

to the quality of distributed computing platforms into

various problems and difficult application

implementation. In fact, the types of distributed

computing platforms are diverse and coupled, and the

distributed computing platforms are in urgent need of

comprehensive optimization strategies to conduct

spectrum management of the distributed computing

platforms to prevent the waste of distributed

computing resources (Guan, De, et al. 2019), (Huang,

Guo, et al. 2017). The construction of the distributed

computing platform is mostly laid in a controllable

environment, while the distributed computing

platform plays a role in frequency band management

according to the basic data set (Jindal, Gerndt, et al.

2021), (Liu, Zhu, et al. 2019), (Lv, 2020). However,

there are some problems in the application of

distributed computing platform, which bring

obstacles to the use of distributed computing

platform. Therefore, this paper studies the application

of spectrum management and optimization algorithm

in distributed computing platform, and verifies the

effectiveness of the proposed method (Naranjo,

Cores, et al. 2013), (Tang, Jiang, et al. 2020),

(Vonschilling, Levis, et al. 1995).

2 RELATED WORKS

At present, many experts have studied the application

of spectrum management and optimization algorithm

in the distributed computing platform, and also put

forward some research results. Using the traditional

sequence framework of spectrum in distributed

computing platform management and optimization

algorithm, the network framework for spectrum

information acquisition ability is poor, has certain

limitations, therefore, according to the shortcomings

of traditional platform design and implementation,

build distributed computing platform spectrum

Ding, R., Liang, C. and Zhao, Y.

Optimization and Implementation of the Spectrum Management and Optimization Algorithm in the Distributed Computing Platform.

DOI: 10.5220/0013535900004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 85-90

ISBN: 978-989-758-763-4

model, the spectrum mechanism and combination, by

combining the application algorithm of spectrum

management and optimization algorithm application

system, thus improving the accuracy of the

distributed computing platform spectrum

management and optimization algorithm application.

The simulation experiment results show that the

accuracy of the proposed algorithm is improved to

different degrees compared with other algorithms,

which proves that the overall performance of the

proposed algorithm is better, but the method has the

problem of poor practicability. Establishment of

distributed computing platform based on spectrum

management and optimization algorithm, and

implementation, by the spectrum based on

management and optimization algorithm in the

supervision stage, obtain the characteristics of the

distributed computing platform application sample,

the application information into the distributed

computing platform, optimize the extraction effect of

the spectrum vector characteristics, the spectrum

management and optimization algorithm application

characteristics into the distributed computing

platform, realize the spectrum management and

optimization algorithm application results. The

experimental results show that the constructed

distributed computing platform can accurately apply

the information, and the application spectrum and the

amount of information will not interfere with the

distributed computing platform. The use effect of the

distributed computing platform is good, but there are

still some limitations.

Put forward based on spectrum management and

optimization algorithm, first of all, the distributed

platform application system network architecture

design, establish spectrum management mapping

model analyzes the spectrum characteristics, the

spectrum management characteristic information

clustering and judge the characteristic attributes,

realize the spectrum management and optimization

algorithm application characteristic analysis,

complete the distributed computing platform

spectrum management and optimization algorithm

application. Through simulation experiment, it is

proved that the platform can obtain the optimal

solution application, but there is the problem of poor

application accuracy.

3 METHODS

In order to ensure that the distributed computing

platform in spectrum management and optimization

algorithm application accuracy, using, spectrum

management and optimization algorithm, based on

the acquisition of distributed computing platform

application information with spectrum management

information, build spectrum management and

optimization algorithm of distributed computing

platform sample spectrum management

characteristics model. Before the application of the

spectrum management and optimization algorithm,

import the correlation feature mapping between the

information spectrum management and the

reorganization of the related feature parameters, and

obtain the feature correlation calculation of the

spectrum management feature model of the

application information of the collected spectrum

management and optimization algorithm, as shown in

formula (1):

)

( VXZ +=

)

In the formula, X represents the balance

coefficient of the spectrum management feature

information collected by distributed computing

platform, C represents the constant of the analytical

adaptation of the spectrum management feature

information collected by distributed computing

platform, and V represents the density set of the total

application of the spectrum management feature

information collected by distributed computing

platform.

In distributed computing platform application

spectrum management characteristics under the

condition of fuzzy, through the fusion model can

draw the correlation coefficient space information

distribution characteristics, the application spectrum

management information analysis can obtain

correlation coefficient space mapping index, under

the correlation coefficient space with linear

relationship, can get the distribution weight control

function, as shown in the formula (2):

)(

GFDS

−

−+

)

Formula, S represents the characteristic

coefficient of information corresponding model

coefficient, D represents the spectrum management

features of mapping reorganization features, F

represents spectrum management and optimization

algorithm application features of the initial quantity,

G represents, distribution mapping index, thus the

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distributed computing platform application mapping

function model, as shown in the formula (3):

log

1(log ++

）

)

In the formula, D represents the amount of map

reorganized by the spectrum management feature, F

represents the initial amount of features applied by

the spectrum management and optimization

algorithm, and H represents the amount of spectrum

management features informative in the application

of the spectrum management and optimization

algorithm. In the information component processing

feature fusion mechanism, obtain the initial spectrum

management features of the component set, according

to the correspondence of spectrum management

mapping, introduce the characteristics of the

component space sequence, the characteristics of

application information parameters feature fusion, get

the initial characteristic model expression, as shown

in the formula (4):

WEQ +=

)

In the formula, W represents the spectrum

management and optimization algorithm features

sequence corresponding information component

correlation distribution set, E represents the current

spectrum management and optimization algorithm

application information characteristic parameter

distribution set, R / T represents the distribution

coefficient of spectrum management and

optimization algorithm application features fusion, by

feature analysis after the cluster extraction, and get

the matrix of spectrum management and optimization

algorithm application spectrum management features

the initial model output, as shown in the formula (5):













−−−

−

11210

12420

120

0000

）（）（

）（

kkk

rrrr







)

In the formula, R represents the initial coefficient

of the feature information association of the imported

spectrum management corresponding to the

optimization algorithm. Based on the above analysis

of the spectrum management characteristics of the

spectrum management and optimization algorithm in

the distributed computing platform, the conditional

probability of the spectrum management and

optimization algorithm can be obtained according to

the state of the distributed computing platform, as

shown in formula (6):

)(),|

1 jjj

HFGDDS =

−

（

)

In the formula, S represents the model coefficient

corresponding to the characteristic coefficient of

information, D represents the recombination feature

of the spectrum management feature, G represents the

distribution mapping index, F represents the

comprehensive model applied by the spectrum

management and optimization algorithm, as shown in

formula (7):

),(

11 −−

jjj

VCXZ

)

In the formula, X represents the basic factor of

using the spectrum management and optimization

algorithm, C represents the output of the known

distributed computing platform information, and V

represents the distributed spectrum factor of the

applied output information. Using the expansion

vector conversion, it is concluded that the information

source input applied by the spectrum management

and optimization algorithm is the data source, as

shown in formula (8):

{}

rrrrR ,,,,

321

=

)

In the formula, R represents the information

volume applied by the spectrum management and

optimization algorithm. In the initial stage of

application, the information data source is applied to

develop the optimization model to obtain the

corresponding information, as shown in formula (9):

PII

)(

×•=

)

In the formula, T represents the basic criteria for

the application of spectrum management and

optimization algorithm, Y represents the feature

module of spectrum management, I represents the set

of information sources, P represents the information

sources after segmentation, and R represents the

sequence of applied information. Define the

information applied to the spectrum management and

optimization algorithm, and select the information set

of the spectrum management and optimization

algorithm, as shown in formula (10):

Optimization and Implementation of the Spectrum Management and Optimization Algorithm in the Distributed Computing Platform

)(

gPS

TRE

•

)

In the formula, ER(T) represents, the

supplementary component of frequency spectrum

management and optimization algorithm application,

Y(U) represents the number of frequency spectrum

management applied by frequency spectrum

management and optimization algorithm, PS (g)

represents, and the frequency spectrum information

identified.

4 RESULTS AND DISCUSSION

In order to verify the effectiveness of the application

of spectrum management and optimization algorithm

in the proposed distributed computing platform, the

simulation experiment is conducted, and the

experimental environmental parameters are shown in

Table 1.

Table 1: The experimental environmental parameters.

Paramete

content

Distributed computing platform 342.162.12

Spectrum management and

optimization algorithm

315.264.25

Basic platform environment 367.261.42

Information collection processing 409.136.12

As shown in Table 1, the application test

experiment of spectrum management and

optimization algorithm of distributed computing

platform must pay attention to the randomness of the

selection of test objects. In order to ensure the

accuracy of this study, the research objects must be

limited, as shown in Table 2.

According to the parameter setting in Table 2, the

spectrum management and optimization algorithm

application test of the distributed computing platform

is used, and the application data of the platform are

tested. The comparison results are shown in Figure 1.

Table 2: Detailed settings of the analyzed data.

Category of tests Types of spectrum

management

lications

monitoring

parameter

Distributed

computing platform

A frequency

spectru

3621 0.225×100

-3

Distributed

computing platform

B frequency

ectru

3516 0.215×100

-3

Distributed

computing platform

C frequency

ectru

3529 0.225×00

-3

Distributed

computing platform

D-spectru

3606 0.215×100

-3

Distributed

computing platform

E spectru

3597 0.225×100

-3

Figure 1: Convergence curve for the application of the

spectrum management and optimization algorithm.

Distributed computing platform in spectrum

management and optimization algorithm application

the distribution of data sources, can effectively reflect

the application results and link between spectrum

management, spectrum, distribution is relatively

loose, means the application data source and

spectrum management does not fit, spectrum

distribution, application data source and spectrum

management, spectrum information more coherent.

Based on the analysis of Figure 1, it can be seen that

the spectrum management and optimization

algorithm proposed in this paper has better accuracy

of spectrum information capture, the application

structure is more coherent, and more in line with the

requirements of spectrum management. This shows

that the proposed algorithm has more practical value.

Taking the amount of spectrum information as an

independent variable, the proposed application

algorithm is used to test the speed of distributed

computing platform application. The spectrum

information matching rate of the proposed applied

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algorithm is compared, and the comparison results are

shown in Figure 2.

Figure 2: Comparison of the applied matching curves for

the different algorithms.

It can be analyzed from figure 2, based on spectrum

management and optimization algorithm of

frequency data statistics, although not get the

complete spectrum information, the spectrum

management and optimization algorithm application

speed control, with the increase of the number of

information and gradually slow, the overall

performance of the application algorithm is better

than traditional algorithm, but because not extract the

spectrum management characteristics of distributed

computing platform, the spectrum management and

optimization algorithm application become more

complicated. The proposed spectrum management

and optimization algorithm is combined with the

advantages of other traditional algorithms to

accelerate the application of the spectrum

management and optimization algorithm in the

distributed computing platform are shown in Fig. 2.

As can be seen from Figure 3, the matching degree

of the application process of spectrum management

and optimization algorithm in the distributed

computing platform is used to analyze the application

of the proposed spectrum management and

optimization algorithm. Regardless of the spectrum

information matching application, the matching

degree change range and distribution range are better

than the traditional algorithm. This shows that the

spectrum management and optimization algorithm

proposed in this paper is applied to the spectrum

management and optimization algorithm in the

distributed computing platform, which can ensure the

stable output of the application effect, and the overall

control effect is optimal.

Figure 3: Comparison of matching degree between

spectrum management and optimization algorithm in

distributed computing platform

5 CONCLUSIONS

In summary, for the application analysis of spectrum

management and optimization algorithm in

distributed computing platform, this paper conducts

in-depth research on the application of spectrum

management and optimization algorithm in

distributed computing platform. Firstly, the spectrum

management and optimization algorithm is

introduced, and the initial model and spectrum

management feature of the distributed computing

platform are extracted to complete the application

analysis of the spectrum management and

optimization algorithm in the distributed computing

platform. Then, the spectrum management and

optimization algorithm is used to comprehensively

design the distributed computing platform to build a

scientific and accurate distributed computing

platform. The results of the simulation experiment

show that the proposed algorithm based on spectrum

management and optimization can improve the

application effect of distributed computing platform,

and the overall stability and better, effectively

improve the accuracy and practice rate of spectrum

information management application. Therefore, it is

necessary to optimize and realize the spectrum

management and optimization algorithm in the

distributed computing platform.

Optimization and Implementation of the Spectrum Management and Optimization Algorithm in the Distributed Computing Platform

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