This work is licensed under a Creative Commons
Attribution-NonCommercial-ShareAlike 4.0 International License.
64
S.P. Sekwatlakwatla, and V. Malele,
“An approach for optimizing resource allocation and usage in cloud computing systems by predicting tracflow”,
Latin-American Journal of Computing (LAJC), vol. 11, no. 1, 2024
An approach for
optimizing resource
allocation and usage
in cloud computing
systems by predicting
traffic flow
ARTICLE HISTORY
Received 12 August 2023
Accepted 23 October 2023
Published 08 January 2024
Sello Prince Sekwatlakwatla
North-West University
Vanderbijlpark, South Africa
sek.prince@gmail.com
ORCID: 0009-0005-1931-5349
Vusumuzi Malele
North-West University
Vanderbijlpark, South Africa
Vusi.Malele@nwu.ac.za
ORCID: 0000-0001-6803-9030
ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2024
This work is licensed under a Creative Commons
Attribution-NonCommercial-ShareAlike 4.0 International License.
ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2024
65
DOI:
LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XI, Issue 1, January 2024
10.5281/zenodo.10402257
LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XI, Issue 1, January - June 2024
An Approach for Optimizing Resource Allocation
and Usage in Cloud Computing Systems by
Predicting Traffic Flow
Sello Prince Sekwatlakwatla
North-West University
Department of Computer Science and
Information Systems
Vanderbijlpark, South Africa
sek.prince@gmail.com
Vusumuzi Malele
North-West University
Department of Computer Science and
Information Systems
Vanderbijlpark, South Africa
Vusi.Malele@nwu.ac.za
Abstract—The cloud provides computing resources as a service
(scalable and cost-effective storage, management, and accessibility
of data and applications) through the Internet. Even though cloud
computing offers many opportunities for ICT (information and
communication technology), many issues still remain, and the
increasing demand for resource management and traffic flow is also
becoming increasingly problematic. The amount of data in the cloud
computing environment is increasing on a daily basis, which
increases data traffic flow. Due to this problem, clients complained
about the network speed. Autoregressive Integrated Moving
Average (ARIMA), Monte Carlo, Extreme gradient boosting
regression (XGBoost), is used in this paper for predicting traffic
flow. A Monte Carlo prediction of 84% outperformed ARIMA's
prediction of 79.8% and XGBoost's prediction of 71.5%, indicating
that Monte Carlo is more accurate than other models when
predicting traffic flow in organizational cloud computing systems.
A machine learning model will be used for future studies, along with
hourly monitoring and resource allocation.
Keywords—Monte Carlo technique, Autoregressive integrated
moving average (ARIMA) and Extreme gradient boosting regression
I. I
NTRODUCTION
As telecommunication networks play an increasingly
important role in deploying cloud applications and services in
data centers, converged network traffic is growing each year
as the cloud computing concept becomes more widely used as
a means of providing access to applications and services[1].
Recently, cloud computing has emerged as an innovative
way to deliver and host services via the Internet. As a result,
business owners are attracted to cloud computing because they
do not need to plan ahead for provisioning, and they can start
with the fewest resources and expand them only when demand
increases[1-3].
Introducing advanced technologies like cloud computing
has revolutionized businesses and industries worldwide. It has
created a new way of storing and accessing information
online[4].Despite offering tremendous opportunities for the IT
industry, cloud computing technologies are still in their
infancy, with many issues yet to be resolved. It is an
increasingly Thoughtful problem for the ICT (Information and
Communication Technology) infrastructure organization to
ignore the growing traffic flow and resource management
demands.
Managing and maintaining ICT resources is easier and
more efficient thanks to cloud computing [6]. The use of cloud
computing by many organizations is growing, so if a tool does
not exist to forecast cloud computing traffic, resource
allocation to clients will be inefficient [8].Cloud computing is
complicated by the inconsistent flow of network traffic, which
makes it difficult to predict which network resources will meet
the needs of all network clients at a particular moment. Clients
complained about slow system times, application timeouts,
and high bandwidth usage due to inconsistencies in traffic
flow [9].
In recent literature, many cloud computing service
providers have been finding it hard to allocate the resources
they need to meet their clients' needs, causing system
bottlenecks, especially during peak periods [5].
The main contributions of this paper are the simulation
experiments conducted and the bibliometric analysis
conducted. Monte Carlo, Extreme Gradient Boosting
(XGBoost), and Autoregressive Integrated Moving Average
(ARIMA) were used to make predictions.
Using a cloud network services traffic flow dataset, this
paper compares three prediction techniques used to overcome
this challenge. It is structured as follows: an introduction,
related work, methods ,simulations , results, and conclusions.
II. R
ELATED
W
ORK
A. Bibliometric analysis of the proposed techniques
For exploring and analyzing large amounts of scientific
data, bibliometric analysis has become a popular and rigorous
method [1]. As a result, analysis is able to uncover the
evolutionary nuances of specific fields. Hence, this paper
examines the proposed prediction technique to examine the
main key area of this approach.
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DOI:
LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XI, Issue 1, January 2024
10.5281/zenodo.10402257
S.P. Sekwatlakwatla, and V. Malele,
“An approach for optimizing resource allocation and usage in cloud computing systems by predicting traffic flow”,
Latin-American Journal of Computing (LAJC), vol. 11, no. 1, 2024.
Fig. 1. Summary of the scopus view
As a result of bibliometric analysis techniques,
researchers can gain insights into the selected research area
very quickly. Search queries were executed on the Scopus
database, and 44 documents were extracted between 2019 and
2023. According to Fig 1, out of 255 authors and 176 author
keywords (DE), there is a high prediction rate of 93.43%.
Fig. 2. Most Frequent Words
With reference to the Monte Carlo technique,
Autoregressive integrated moving average (ARIMA) and
Extreme gradient boosting regression (
(XGBoost),
Fig 2
presents the word cloud generated using the authors'
keywords. Forecasting words are shown in a larger size, such
as "machine learning," "learning systems," and "time series"
and "time series analysis". Prediction was the area of focus for
three proposed models.
B. Gap analysis on the related techniques
A cloud computing service provider must provide
adequate and accurate traffic prediction to meet customer
needs and support organizations effectively [2-3]. Cloud
computing service providers estimate requests and the amount
of data they must move frequently to predict system traffic
flow. Intelligent transportation cyber-physical cloud control
system (ITCPCCS) improved accuracy in cloud control
traffic; however, it was difficult to predict in real time since
the technique is in the development stage [2].
In recent years, Monte Carlo simulation Vibrational mode
decomposition (VMD) has been shown to be a more accurate
tool for predicting short-term conditions than long-term
models. Limit forecasts to short-term deterministic and
probabilistic models [5]. The accuracy of forecasts is tested
using publicly available Google trace data and Bit Brain data,
but workloads in dynamic clouds may experience substantial
forecast errors [13].
There is an overlap between the highest values of
subseries, models orders, and weights that are time-dependent
and auto-regressive. Auto-regressive integrated moving
average models (ARIMAs) [14] produce more accurate
forecasts and are more efficient to compute. As most
applications and information will have to be moved repeatedly
in the future, it is best to move them daily. The activity
stream's predictions have been validated in numerous studies
[13] and 85% of the predictions have been met. In spite of this,
there has been no progress in implementing a framework that
predicts traffic using an application framework. Since cloud
computing is a continuous process, it is difficult to predict
traffic 100% accurately [5-6].
The stationary time series are assumed to remain
unchanged over time in this method, which is a Markov chain
Monte Carlo approach and a Seasonal Autoregressive
Integrated Moving Average (SARIMA) model [6]. In
comparison to the existing method, the proposed method has
a lower error rate. Artificial Neural Network (ANN) [7] has
the ability to predict accurately with a small error or small
neurons. It is concise and straightforward to perform Monte
Carlo simulations of GP distributions, and errors increase and
decrease with sample size [8].
Traffic flow predictions can be enhanced with Multiple
Linear Regression Unit-M4ulti Region Correlation (MRC-
MLRU) models [1], which are useful when traffic data is
limited. eXtreme Gradient Boosting (XG Boost) [9] this
method provides better prediction performance with an R2 of
0.992 and 0.949, respectively. The baseline model tends to be
over fitted. This is an improved multivariate linear regression
variable parameter spatiotemporal (MLR-VPST) zoning
model [11]. Both quality and accuracy have improved, and
errors are no longer beyond the process limits [15].
III. M
ETHODS
A. Probabilistic Models
To quantify uncertainty, a probabilistic model integrates
the first principle knowledge with data to capture a
distribution of state transitions between samples in a batch
run by making predictions based on the model predictions. A
probability model calculates the probability of certain events
occurring rather than monitoring actual data to look for
events and data points that conform to a set of rules defined
by historical analysis-Monte Carlo.
Monte Carlo technique is a computer-based mathematical
technique that accounts for risk quantitatively in forecasting
and decision-making [13].
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LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XI, Issue 1, January 2024
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LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XI, Issue 1, January - June 2024
Fig. 3. The simple principle of the Monte Carlo simulation [9]
In order to predict the outcome of a Monte Carlo
simulation, the following steps are proposed Fig 3:
Process 1: The first process is to generate a regression
parametric.
Technique, p=f(x_1,x_2…….x_r ).
Process 2: Set up input generator, x_(c1,) x_(c2,)…….x_cq
Process 3: Model evaluation and data storage as y_c
Process 4: Process 2 and 3 must be repeated i=1 to n
Process 5: Calculate confidence intervals, complete
statistics,and histograms based on the results.
B. Machine Learning/Data Mining
In machine learning, data are used to create mathematical
models that predict or decide without explicit programming.
The algorithms use "training data" to make predictions
Extreme gradient boosting regression (XGBoost), in time
series forecasting, models like Extreme Gradient Boosting
(XGBoost) can provide reasonable forecasts without tweaking
hyper parameters [9]. This is a machine learning algorithm
that uses gradient boosting to solve a variety of problems in
machine learning.
Fig. 4. The simple principle of the XGBoost [11]
It is shown in Fig 4 that XGBoost is an the algorithm
moves through iterations, it learns from the residuals of its
neighbors. Rather than accepting the majority of the prediction
results in Random Forest, it produces a more accurate
prediction in this algorithm [1].
=
(
)
,
, (1)
Where In regression trees, the space of trees is defined as,
Assumes the form of a tree, so
(
)
is the outcome of
tree , and
Evaluate predicted by
request
(
)
,
C. Statistical Analysis
The Autoregressive integrated moving average
(ARIMA) is an advanced analytics technique that uses
historical data and statistical models to predict future
outcomes. Serial correlation is used to forecast or predict
future outcomes with autoregressive integrated moving
averages (ARIMA) [4].In equation 2, the process of
calculating autoregressive integrated moving averages
(ARIMA) is illustrated by explaining past observations and
random errors.
=
+
+
… +
+
+
+
… +
(2)
and
in Equation. (2) this is a representation of the real value and
the error at a particular time interval , respectively, and .
(= 1,2 … . )
and
(
= 1,2 … . .
)
During auto regression, time series is a linear
regression of the p previous values accompanied by the error.Moving
average MA(q) describes the current rate of a time series in terms of an error
at time t, and q earlier errors.
IV. D
ATASET
As cloud computing grows, its traffic flow increases every
day. As well as uncoordinated traffic signal control and a lack
of real-time data, the constant availability of the system is a
critical element that cannot be ignored. Nowadays, traffic
congestion has a huge impact.
In this study, data were obtained from a South African,
Sandton, and automotive industry company with an IT
department. As the company outsources other IT
infrastructure companies, we are dealing with Cloud
computing systems within the organization that are
experiencing resource allocation problems. This study
ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2024
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DOI:
LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XI, Issue 1, January 2024
10.5281/zenodo.10402257
S.P. Sekwatlakwatla, and V. Malele,
“An approach for optimizing resource allocation and usage in cloud computing systems by predicting traffic flow”,
Latin-American Journal of Computing (LAJC), vol. 11, no. 1, 2024.
collected comprehensive data from January 1, 2017, to
December 31, 2022 (representing daily page loads, unique
visitors, new visitors, and returning visitors).
V. S
IMULATED
S
CENARIOS
As illustrated in Fig 5, a three-stage research design is
proposed in this study as well as three predictive methods.
Fig. 5. Proposed technique
The simulation was performed using the Time Series Lab and
Crystal Ball.
A model receives data and predicts it, then the model is
evaluated and the results are presented in Fig 5.
Method 1
Data > Prediction Techniques > Probabilistic Models >
Monte Carlo Techniques > Evaluations > Results.
Method 2
Data > Prediction Techniques > Machine Learning >
XGBoost Techniques > Evaluations > Results.
Method 3
Data > Prediction Techniques > Statistical Analysis
Techniques> Arima> Evaluations > Results.
VI. R
ESULTS
A. Evaluation Measures
In evaluation measures, errors between expected and real
qualities are defined numerically [15]. The difference between
them indicates how well a model did. The data set presented
to the network was used to evaluate the performance of the
matched network. Mean absolute percentage error (MAPE)
was applied to Monte Carlo, ARIMA, and XGBoost
techniques in this regard. These measures are defined by
equations.
MAPE =
|
|
x 100 (3)
Where
Value predicted for observation ,
The actual observation value , and
Number of observations.
These prediction analysis techniques have been used to
improve cloud network traffic flow predictions. Time Series
Lab, Crystal Ball, and real data were used to simulate traffic
flow and determine the effectiveness of the proposed model.
B. Monte Carlo technique results
Fig. 6. The normal distribution of the observed data was calculated every
20 seconds according to a 2.2 minute interval.
Fig. 7. Observed value
In Fig. 7, when traffic volume was low, the model accuracy
increased; however, when traffic volume increased, the model
accuracy also decreased, and the prediction was 84%, Fig 7
illustrates Monte Carlo results for the observed data. The
normal distribution for the observed data was calculated every
20 seconds according to a 2.2 minute interval as explained in
Fig 8. The MAPE prediction errors is 4.49 %.
C. Autoregressive Integrated Moving Averages (ARIMA)
As shown in Fig 8, the results of the Autoregressive
Integrated Moving Averages (ARIMA) model were produced
within 0.40 seconds, which was 0.40 seconds longer than the
previous model. The MAPE prediction error is 5.59 %.
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LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XI, Issue 1, January 2024
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LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XI, Issue 1, January - June 2024
Fig. 8. Autoregressive Integrated Moving Averages (ARIMA)
In Fig. 8, when traffic volume increased, the model accuracy
increased; however, when traffic volume decreased, the model
accuracy also decreased, and the prediction was 79,8%.
D. Extreme Gradient Boosting Regression (XGBoost)
As shown in Fig 9, extreme gradient boosting (XGBoost)
reduced training time to 10 seconds, however, prediction
accuracy decreased with a minimum in error evaluation of
MAPE 5.88
Fig. 9. The XGBoost prediction.
In Fig. 9, when traffic volume increased, the model accuracy
increased; however, when traffic volume decreased, the model
accuracy also decreased, and the prediction was 71,5%.
VII. D
ISCUSSION
TABLE I. C
OMPARISON OF
P
REDICTION
T
ECHNIQUE
E
RRORS
Comparison of Techniques Errors
Evaluation Items
Monte
Carlo
techniques
ARIMA
XGBoost
MAPE
4.49
4.59
5.88
Training time
~120
obs/sec
~0.40
obs/sec
10.43
obs/sec
In order to identify the best performing techniques,
prediction analysis techniques, Monte Carlo techniques,
ARIMA techniques, and XGBoost techniques were applied
to a real dataset from an online cloud networking application
system experiencing traffic congestion.
From 2017 to 2022, a comprehensive set of data was
collected on , unique visitors, first-time visitors, and returning
visitors on a daily basis.In Table 2, the Monte Carlo training
method showed better performance than the ARIMA and
XGBoost techniques. It improves prediction accuracy with
the minimum amount of errors. Hence, Monte Carlo
techniques should be incorporated into traffic prediction
models or architectures
VIII. C
ONCLUSION
The quality of service can only be improved if future
workloads are accurately forecasted and resources allocated
optimally. By using this predictive analysis, cloud providers
can prevent a wide range of losses, such as service outages,
excessive or inadequate provisioning of cloud resources, and
customer losses.
This paper compares three predictive data analytics
techniques to determine how well a cloud computing system
predicts future traffic parameters based on their data analytics.
A bibliometric analysis review is also conducted to analyses
the focus area of the proposed technique, and the findings will
contribute to the design of a predictive model for managing
cloud computing resources. As a result of traffic control
systems' ability to predict future values of traffic parameters,
their performance can be improved.
Based on bibliometric analysis, it has been found that the
proposed methods are relevant for predicting cloud computing
traffic flow, and Monte Carlo techniques are more efficient,
than Extreme Gradient Boosting (XGBoost) and
Autoregressive integrated moving averages (ARIMA) for
analyzing data at a yearly level. Using hourly, daily, weekly,
and monthly traffic predictions, future work will determine the
most efficient time to allocate cloud computing resources. In
addition, other predictive techniques will be included in order
to determine which ones will be the most effective in
determining the best real-time resource management in cloud
computing systems.
A Monte Carlo prediction of 84% outperformed ARIMA's
prediction of 79.8% and XGBoost's prediction of 71.5%,
indicating that Monte Carlo is more accurate than other
models when predicting traffic flow in organizational cloud
computing systems. A machine learning model will be used
for future studies, along with hourly monitoring and resource
allocation.
C
ONFLICT OF
I
NTEREST
No conflict of interest has been declared by any of the authors.
A
UTHOR
C
ONTRIBUTIONS
Ph.D. students developed original drafts after consulting with
their supervisors. They conceptualized the methodology,
collected the data, prepared the experimental platform, and
conducted Time Series Lab and Crystal Ball simulations.
ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2024
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DOI:
LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XI, Issue 1, January 2024
10.5281/zenodo.10402257
S.P. Sekwatlakwatla, and V. Malele,
“An approach for optimizing resource allocation and usage in cloud computing systems by predicting traffic flow”,
Latin-American Journal of Computing (LAJC), vol. 11, no. 1, 2024.
Following the presentation, supervisors gave significant
inputs to the draft, which changed the methodology and
provided directions toward developing a traffic analysis
model for optimizing resource allocation and utilization in
cloud computing systems.
A
CKNOWLEDGMENT
The article was written as part of PhD study in Computer
Science and Information Systems with Information
Technology.
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LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XI, Issue 1, Januay 2024
AUTHORS
A registered PHD student at North West University with over 13 years
of IT experience, Sello has expertise in Integration Management,
System Implementation, and System Release Management.
A senior researcher and Postgraduate supervisor at North West
University. An experienced engineer, teacher, research professional and
manager with more than 25 years of experience in the ICT industry.
Sello Prince Sekwatlakwatla
Vusumuzi Malele
S.P. Sekwatlakwatla, and V. Malele,
“An approach for optimizing resource allocation and usage in cloud computing systems by predicting trac flow”,
Latin-American Journal of Computing (LAJC), vol. 11, no. 1, 2024
