Resource Load Balancing on Cloud Infrastructure for Subscriber

Management in Comparison with Raw Unbalanced Data for

Calculation of Energy Consumption

V. Venkatesh and A. Shri Vindhya

Department of Computer Science and Engineering, Saveetha University, Chennai, India

Keywords: Advertising, Business, Expectation Maximization Clustering Algorithm, Fractionation, Marketing,

Novel K-Means Clustering Algorithm, Patron.

Abstract: This study compares the novel K-Means clustering method against the more popular Expectation-

Maximization Clustering technique in order to envision whether one produces more accurate results when

used to partition patrons' online social network activity. Materials and Methods: Extensive testing was

conducted to determine the accuracy percentages of both the K-Means clustering method and the Expectation-

Maximization clustering algorithm. The sample size used for each test was 110, and for the Expectation-

Maximization algorithm, a G power (value) of 0.6 was employed. Results: According to the results, the novel

K-Means clustering approach is superior to the Expectation-Maximization Clustering methodology in terms

of accuracy (87.97% vs. 79.77%). At a significance level of 0.001 (p < 0.05), the data strongly indicates a

noteworthy distinction between the two groups. When compared to the Expectation-Maximization clustering

approach, the novel K-Means technique fared very well.

1 INTRODUCTION

The primary goal of these results is to utilize the novel

K-Means grouping strategy and the thickness based

spatial bunching calculation to look at the division of

supporters' activities in web-based informal

organizations. The experiment's stated goal is to

"increase the accuracy of patron fractionation"

(Tabianan et. al, 2022). Differentiating clients into

subgroups using demographics, psychographics, and

other characteristics is called "patron fractionation"

(for example, grouping patrons by age). In other

words, it's a technique used by companies to learn

more about their clientele. A better understanding of

the differences across patron subsets is helpful for

making strategic decisions regarding product

development and marketing (Sivaguru, M. et.al,

2022). The quantity of available patron data will

determine the breadth of the possible fractionations.

A user's gender, interests, or age are only the

beginning; later on, criteria such as "time since the

user accessed our app" or "time spent on website X"

are taken into account. The applications of this

research helps in finding an optimal number of unique

patron groups and separate them based on their

attributes and concentrate on that particular area to

improve sales and provide the patrons what they want

(G. Ramkumar et al 2021).

The Implications of Finding The deployment of

patron fractionation paves the way for several new

business opportunities (Lefait et al 2010). Many

aspects of business, including budgeting, product

development, marketing, patron service, and sales,

may be enhanced by optimization. There are a total of

4,22,00,000 papers on the topic of patron activity

fractionation in online social networks, with

16,22,00,000 appearing in IEEE Xplore and the rest

in both that and Google Scholar. Now, let's examine

these benefits in more depth. Budgeting: It's

frustrating when advertising efforts don't result in

new business (Pradhan et al, Rahul et al 2021). Most

companies don't have unlimited funds for advertising,

therefore every dollar counts. fractionation helps to

prioritize the marketing efforts so that putting money

where money will do the most good, towards the

patrons who are most likely to return the investment.

The process of creating product designs. patron

fractionation facilitates the process of gaining insight

into consumers' wants and demands . They may zero

in on the most engaged audience to fine-tune apps or

services for them.

354

Venkatesh, V. and Vindhya, A.

Resource Load Balancing on Cloud Infrastructure for Subscriber Management in Comparison with Raw Unbalanced Data for Calculation of Energy Consumption.

DOI: 10.5220/0012772100003739

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

In Proceedings of the 1st International Conference on Artiﬁcial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics (AI4IoT 2023), pages 354-359

ISBN: 978-989-758-661-3

Figure 1: Procedure for K-Means clustering algorithm.

Figure 2: Procedure for Expectation-Maximization through Clustering Algorithm.

Figure 3: Schematic diagram for patron’s fractionation.

Resource Load Balancing on Cloud Infrastructure for Subscriber Management in Comparison with Raw Unbalanced Data for Calculation of

Energy Consumption

355

Figure 4: Expectation maximization comparison bar chart.

Consumers may be more accurately categorized

for more effective advertising planning. Sales have

become more commonplace in the commercial

software and online retail industries in recent years

(Liu et al, Chaohua 2011). Assuming to make the

right offer at the right moment, a patron is likely to

make a buy. Using patron fractionation, they may

tailor promotions to each individual client. Marketers

may quickly benefit from fractionation because it

allows them to tailor campaigns to specific groups of

patrons and deliver them through the channels of their

choosing. satisfaction of the consumer market: By

doing market research, may learn more about the

requirements of certain patron demographics. respect

the most about the company. With this information,

they may provide services and goods that are

specifically designed to meet the needs of target

market. The main aim of this research is to compares

the novel K-Means clustering method against the

more popular Expectation-Maximization clustering

technique in order to determine whether one produces

more accurate results when used to partition patrons'

online social network activity.

The group's expertise and experience in the field

have resulted in a number of scholarly works and the

study's main limitation is that it recommends against

providing each and every patron with a similar item

variation, email, instant message, or advertising

(Barga et.al 2015). There is a wide range of patron

needs. In the company, a "one size fits all" approach

seldom works. It usually results in fewer clicks, fewer

patrons, and less money made. The solution to this

problem is the patrons' ability to divide their

purchases.

2 MATERIALS AND METHODS

The Robotic Laboratory at SIMATS, is where this

study was conducted. There are two teams in the

planned task. Uses novel K-Means for the first set,

then Expectation-Maximization for the second.

Sample size of 132, 90% confidence interval, 60% G

power, and a fixed maximum tolerated error of 0.05

were used to compare the novel K-Means method

with the Expectation-Maximization clustering

algorithm (Seybold et.al, Patricia. (2002)).

After collecting datasets train.csv and test.csv, it

is preprocessed and cleaned to eliminate any

irrelevant or unnecessary information. After the data

has undergone meticulous cleaning and

preprocessing, restated, the sets may be accessed.

novel K-Means and Expectation-Maximization

clustering algorithms' opencv files and libraries were

modified after including new data sets to improve

prediction accuracy. Clusters are determined using an

Expectation-Maximization technique. novel K-

Means and density-based clustering method

clustering procedures are described here.

Devices that meet the requirements of a certain

hardware configuration are referred to as "hardware

configuration devices," and they are assigned a

unique set of specifications and allocations of

computational resources.

2.1 Clustering Using the Novel

K-Means

The novel K-Means algorithm is a centro-metric

technique of clustering. As a result of using this

technique, the dataset is divided into k different

clusters, each containing about the same amount of

data points. For each group, novel K-Means

clustering uses a centroid as its representative

(Hossain et.al, A. S. M. Shahadat et.al, and A. S.

Shahadat Hossain 2017).

novel K-Means Each of the n observations has to

be placed in the cluster that contains the mean (or

prototype) that is closest to its own. Clustering is the

name given to the vector quantization method that

was first used in the field of signal processing.

Pseudocode is shown in Fig. 1.

2.2 EM Clustering: An Expectation-

Maximization Approach

If they have a good idea of how the latent variables'

underlying probability distribution looks, they may

utilize the expectation-maximization process to make

educated guesses about their values (unobservable

AI4IoT 2023 - First International Conference on Artiﬁcial Intelligence for Internet of things (AI4IOT): Accelerating Innovation in Industry

and Consumer Electronics

356

variables that may be deduced from the values of

other observable variables). In reality, this approach

serves as the foundation for other unaided grouping

calculations in the investigation of AI (Santana et.al

2018). Pseudocode is shown in Fig. 2.

2.3 Statistical Analysis

IBM SPSS was the statistical programme of choice

for this investigation. The accuracy numbers are

determined by using the software's descriptive and

group statistics. The significance levels of the tests

performed on the independent samples are

determined. The novel K-Means clustering method

seems to excel in performance than Expectation-

Maximization clustering algorithm on each forum,

according to the two algorithms.

Table 1: Exactness of the novel K-Means.

Iteration

K-Means(%)

Expectation-

Maximization (%)

92.3

86.0

87.5

85.4

89.4

76.0

91.7

75.3

88.6

74.1

90.5

83.5

86.2

81.8

85.8

80.6

84.3

79.4

83.4

75.6

Both the independent and dependent variables

benefit from unique characteristics that aid in

prediction, and the latter have higher accuracy values.

Among the independent variables, patron ID, age, and

gender might influence the dependent variable, which

is average spending. The T-Test for autonomous

examples is run.

3 RESULTS

Table 1 shows the consequences of a reproduced

examination of the exactness of the novel K-Means

and Assumption Expansion bunching techniques.

Table 2 presents rundown insights for both the novel

K-Means and Assumption Augmentation bunching

procedures, uncovering mean upsides of 87.97 and

79.77 and standard deviations of 3.05 and 4.38,

individually. Within Table 3, the significance levels

and standard errors are provided, reflecting the

application of an independent sample T-test to the

two groups. The calculated significance value for

these groups is p=0.001 (p<0.05), signifying their

statistical significance. The clustering methods

pseudocodes are shown in Figures 1 and 2.

The architecture for contrasting two algorithms are

depicted in Figure 3. In this initially patrons’ datasets

are collected. After collecting datasets train.csv and

test.csv, it is preprocessed and cleaned to eliminate any

irrelevant or unnecessary information. Once the data

has been cleaned and preprocessed, the sets may be

accessed. Novel K-Means and Expectation-

Maximization clustering algorithms' opencv files and

libraries were modified after including new data sets to

improve prediction accuracy.

Fig. 4 depicts a comparison of the results of two

algorithms using a bar chart. Accuracy averages

87.97% for novel K-Means and 79.77% for

Expectation-Maximization clustering. The results

suggest that novel K-Means outperforms

Expectation-Maximization when it comes to

clustering algorithm.

Table 2: Accuracy values of Algorithms.

GROUP

Mean(%)

Std.Deviation

Std.

Error

Mean

K-Means

87.97

3.053

0.965

Expectation

Maximization

79.77

4.385

1.386

4 DISCUSSIONS

It seems from this research that the novel K-Means

clustering method is more effective than the

Assumption Augmentation grouping calculation (p =

0.162, Free Example Test). The novel K-Means

outperforms Expectation-Maximization in terms of

accuracy (mean accuracy = 87.97) whereas

Expectation-Maximization only manages 79.77

percent (Zakrzewska, D et al 2005).

Multiple techniques, including DBSCAN,

Agglomerative clustering, Birch, and novel K-Means,

are used in the investigation. Accuracy is measured at

0.79 for Expectation-Maximization and 0.87 for

novel K-Means (Sivakumar, V. L 2022). Novel K-

Means performs better than the Expectation-

Maximization clustering technique, according to a

comparison with that algorithm. The accuracy of

K-Mean is 87.97%, whereas that of Expectation-

Maximization is just 79.77%. In addition, it has

Resource Load Balancing on Cloud Infrastructure for Subscriber Management in Comparison with Raw Unbalanced Data for Calculation of

Energy Consumption

357

Table 3: Statistics of Algorithms.

Equal Variance

Levene’s Test for

Equality of Variance

T-test for Equality of Means

Sig

(2-tailed)

Mean

Difference

Std.

Error

Difference

95% Confidence

Interval of the

Difference

Lower

Upper

Accuracy

Assumed

2.13

.162

4.85

18.00

. 001

8.2

1.68

4.64

11.75

Not Assumed

4.85

16.06

. 001

8.2

1.68

4.61

11.78

produced outcomes that are consistent with our

conclusion (Syaputra et al 2020). They also tested

unsupervised machine learning algorithms using

voice recognition, and found that novel K-Means

outperformed the others with the best accuracy

(Garca, 2022). There was a consensus among four

works and a disagreement among one based on the

study conducted. Furthermore, it seems from the

foregoing talks and data that the novel K-Means

clustering method outperforms the Expectation-

Maximization clustering algorithm under all

circumstances (James, J. 2017).

However, novel K-Means struggles to cluster data

when there are clusters of varying densities and sizes.

In order to cluster such data, it is necessary to

generalize novel K-Means, as described in the

Benefits section. irregularities in groups (Hax 2010).

It's possible for outliers to pull centroids in their

direction, or they might split off into their own group.

There is a wide range of patron needs. A decrease in

engagement, in click-through rates, and in income is

often the result of a "one size fits all" approach to

business. This problem will be resolved thanks to

patron fractionation. The study's main limitation is

that it recommends against providing each and every

patron with a similar item variation, email, instant

message, or advertising (Xue et.al 2022). The goal for

future work is to optimize this model such that it runs

more quickly while still producing accurate results.

It's not a good idea to provide same advertising

(Pramono 2019).

5 CONCLUSIONS

The study on resource load balancing on cloud

infrastructure for subscriber management compared

with raw unbalanced data for the calculation of

energy consumption offers several notable

conclusions:

1. Superiority of Novel K-Means Clustering

Method: The results demonstrate that the novel K-

Means clustering approach outperforms the

Expectation-Maximization (EM) clustering

technique in terms of accuracy. The average

accuracy achieved by the novel K-Means method

is 87.97%, compared to 79.77% with EM

clustering. This indicates that the novel K-Means

method provides more precise partitioning of

patrons' online social network activities.

2. Significance of Cluster Accuracy: The

significance of the difference between the two

clustering methods is statistically supported with

a calculated p-value of 0.001 (p < 0.05). This

indicates a noteworthy distinction between the

accuracy levels achieved by the novel K-Means

and EM clustering algorithms.

3. Implications for Business Optimization:

The study underscores the importance of accurate

patron fractionation for various business

applications, including budgeting, product

development, marketing, patron service, and sales

optimization. By leveraging more accurate

clustering methods such as the novel K-Means

approach, businesses can prioritize marketing

efforts, tailor product designs, and deliver more

effective advertising campaigns.

4. Challenges and Future Directions: While

the novel K-Means clustering method shows

promising results, challenges such as clustering

data with varying densities and sizes remain.

Future work should focus on optimizing

clustering models to address these challenges

while maintaining accuracy and efficiency.

AI4IoT 2023 - First International Conference on Artiﬁcial Intelligence for Internet of things (AI4IOT): Accelerating Innovation in Industry

and Consumer Electronics

358

Additionally, the study highlights the importance

of avoiding a one-size-fits-all approach in

business strategies, emphasizing the need for

personalized approaches enabled by accurate

patron fractionation.

5. Overall Recommendations: Based on the

findings, it is recommended to utilize the novel K-

Means clustering method for patron fractionation

in online social networks due to its superior

accuracy compared to the EM clustering

technique. This recommendation is supported by

the statistical significance of the results and the

potential business benefits associated with more

precise patron segmentation. In conclusion, the

study provides valuable insights into the

effectiveness of different clustering methods for

patron fractionation, highlighting the importance

of accurate data analysis in optimizing business

strategies and resource allocation in cloud

infrastructure management.

REFERENCES

Tabianan et.al, Kayalvily et.al, Shubashini Velu et.al, and

Vinayakumar Ravi. 2022 et.al. “Novel K-Means

Clustering Approach for Intelligent patron fractionation

Using patron Purchase Behavior Data.” Sustainability.

https://doi.org/10.3390/su14127243.

Sivaguru, M. 2022 et.al. “Dynamic patron fractionation: A

Case Study Using the Modified Dynamic Fuzzy c-

Means Clustering Algorithm.” Granular Computing.

https://doi.org/10.1007/s41066-022-00335-0.

G. Ramkumar, R. Thandaiah Prabu, Ngangbam Phalguni

Singh, U. Maheswaran, Experimental analysis of brain

tumor detection system using Machine learning

approach, Materials Today: Proceedings, 2021, ISSN

2214-7853, https://doi.org/10.1016/j.matpr.2021.01.246.

Lefait et al, Guillem et al, and Tahar Kechadi. 2010 et al.

“Patron fractionation Architecture Based on Clustering

Techniques.” 2010 Fourth International Conference on

Digital Society. https://doi.org/10.1109/icds.2010.47.

Pradhan et al, Rahul. 2021 et al. “Patron fractionation Using

Clustering Approach Based on RFM Analysis.” 2021

5th (ISCON).

https://doi.org/10.1109/iscon52037.2021.9702482.

Liu et al, Chaohua. 2011 et al. “patron fractionation and

Evaluation Based on RFM, Cross-Selling and patron

Loyalty.” 2011 ICMSS.

https://doi.org/10.1109/icmss.2011.5998805.

Barga et.al, Roger et.al, Valentine Fontama et.al, and Wee

Hyong Tok. 2015 et.al. “Patron fractionation Models.”

Predictive Analytics with Microsoft Azure Machine

Learning. https://doi.org/10.1007/978-1-4842-1200-

4_10

Hossain et.al, A. S. M. Shahadat et.al, and A. S. Shahadat

Hossain. 2017 et.al. “patron fractionation Using

Centroid Based and Density Based Clustering

Algorithms.” 2017 3rd (EICT).

https://doi.org/10.1109/eict.2017.8275249.

Seybold et.al, Patricia. 2002 et.al. “Designing a patron

Flight Deck (SM) System - patron fractionation.”

https://doi.org/10.1571/fw1-31-02cc.

Santana et.al, Clodomir J. et.al, Pedro Aguiar et.al, and

Carmelo J. A et.al. Bastos-Filho et.al. 2018. “Patron

Fractionation in a Travel Agency Dataset Using

Clustering Algorithms.” 2018 IEEE(LA-CCI).

https://doi.org/10.1109/la-cci.2018.8625252.

Zakrzewska, D et al., and J. Murlewski 2005 et. al.

“Clustering Algorithms for Bank patron fractionation.”

(ISDA’05). https://doi.org/10.1109/isda.2005.33.

Sivakumar, V. L., Nallanathel, M., Ramalakshmi, M., &

Golla, V. (2022). Optimal route selection for the

transmission of natural gas through pipelines in

Tiruchengode Taluk using GIS–a preliminary study.

Materials Today: Proceedings, 50, 576-581..

Syaputra et al, Aldino et al, Zulkarnain et al, and Enrico

Laoh. 2020 et al. “patron fractionation on Returned

Product patrons Using Time Series Clustering

Analysis.” 2020(ICISS).

https://doi.org/10.1109/iciss50791.2020.9307575.

Garca, Kimberly, and Antonio Santos-Silva. 2022. “New

Species and New Records in Neoibidionini and

Hexoplonini (Coleoptera: Cerambycidae:

Cerambycinae).” Zootaxa 5134 (3): 399–414.

James, J., Lakshmi, S. V., & Pandian, P. K. (2017). A

preliminary investigation on the geotechnical properties

of blended solid wastes as synthetic fill material.

International Journal of Technology, 8(3), 466-476.

Hax, Arnoldo C. 2010. “Customer Segmentation and

Customer Value Proposition: The First Critical Task of

Strategy.” The Delta Model.

https://doi.org/10.1007/978-1-4419-1480-4_3.

Xue et.al, Mengfan et.al, Lu Han et.al, Yiran Song et.al, Fan

Rao et.al, and Dongliang Peng. 2022 et.al. “A Fissure-

Aided Registration Approach for Automatic Pulmonary

Lobe fractionation Using Deep Learning.” Sensors 22

(21). https://doi.org/10.3390/s22218560.

Pramono, Pradnya Paramita, Isti Surjandari, and Enrico

Laoh. 2019. “Estimating Customer Segmentation

Based on Customer Lifetime Value Using Two-Stage

Clustering Method.” 2019 16th International

Conference on Service Systems and Service

Management (ICSSSM).

https://doi.org/10.1109/icsssm.2019.8887704.

Resource Load Balancing on Cloud Infrastructure for Subscriber Management in Comparison with Raw Unbalanced Data for Calculation of

Energy Consumption

359