Mathematical Models for Predicting Network Traffic in Cloud Computing Environments

doi:10.21203/rs.3.rs-5000053/v1

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Mathematical Models for Predicting Network Traffic in Cloud Computing Environments

https://doi.org/10.21203/rs.3.rs-5000053/v1

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Effective network traffic prediction is crucial for optimizing resource allocation and ensuring efficient performance in cloud computing environments. In this research, mathematical models are developed to use historical data, factors, and other data to predict future network traffic patterns more effectively. In this context, we then compare an array of time series models such as ARIMA, LSTM, as well as the Prophet model so that we can determine the cloud environments most appropriate for each. These models include time and day of the week as well as the general activities of the users in the network in order to mimic the real flow of network traffics. The experimental results concern the efficiency of the proposed models as opposed to existing approaches and give a lot of information to network administrators and cloud service providers. The outcomes make a significant contribution as to the formulation of intelligent approaches in resource management and improve the dependability and performance of cloud computing environments.

1.1 Background and Motivation

Cloud computing is one of the transformative technologies of the contemporary IT industry in that it provides efficient, flexible, and cheap solutions for the whole population. The aspect of infrastructure, platform, and software facilitating the management of resources, applications, and data is possible through the likes of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) (Aburukba et al., 2021). As cloud services gain more ground in use within organizations, more pressure is placed on traffic within the cloud (Saxena & Singh, 2022). The patterns of cloud systems and operations depend on the implementation of the user’s demands, the types of workloads, and scenarios; therefore, the path of the network traffic has to be optimized (Tuli et al., 2020). Coordinating this traffic is a rather delicate matter of resource allocation and usage of bandwidth and computing power (Li et al., 2024). Unfortunately, cloud networks are not devoid of various problems, such as fluctuations in traffic, lack of available bandwidth, and delays that negatively impact performance and quality of service (Lohrasbinasab et al., 2022). Such problems underscore the importance of having accurate forecasts of the traffic in the network (Shafiq et al., 2022). Forecasting is therefore employed in network planning as a tool by which service providers can avoid congestion and maintain the quality-of-service provision through scheduling the use of resources in relation to the traffic load expected in networks (Abouelyazid, 2022). Traffic forecasting or accurate prediction has turned out to be one of the most important focuses of research with regard to cloud network performance (Balarezo et al., 2022). It has perhaps been established that employing mathematical models may be the best way of solving the problem because they can give near-accurate predictions based on past performance and computation formulas (Omer et al., 2021). But traditional approaches for forecasting network traffic do not suit cloud computing systems mainly because of the complexity and dynamic workloads (Hsieh et al., 2020). Thus, there is increasing interest in more complex, elastic, and adaptive methods to address the features of cloud network traffic analytics.

1.2 Problem Statement

Traffic prediction in cloud computing networks is another fascinating task, and due to the dynamic and distributed structure of the network. The dynamics of workloads, flexibility of resources, and the differences in the types of networks presenting in the network make the traffic hard to model and predict. Whenever the traffic predicted in a network is erroneous poor networks’ performance, traffic congestion, as well as resource misallocation are some of the consequences that may occur in a network. These issues lead to increase costs of operations for the cloud service providers and a degraded level of service for the consumers.

Even though there is a host of existing traffic prediction models in the relevant literature, most of them fail to capture the intricacies of the contemporary cloud computing systems. Currently there are challenges facing the models mainly when it comes to issue of scalability, flexibility particularly when managing change in traffic patterns and achieving a good measure of precision while at the same time taking into consideration computational complexity. However, growth in the richness of the cloud services and the continually diversification of application within the clouds make it more challenging to come up with models that can always predict traffic in real time. Solving these issues calls for solution that will use enhanced mathematical formulations to develop better accuracy and efficiency in the models to be used.

1.3 Objectives

This work seeks to propose, calibrate and verify mathematical formulas that can be used to forecast on network traffic of cloud computing facilities. The overall goal therefore is to offer new or enhanced models suitable for addressing the characteristics of the cloud network traffic; the aspects of variability and uncertainty inherent in such systems.

- Designing new algorithms that would allow prediction of traffic loads in clouds and prevent heavy traffic in the network.

- Making sure the presented models are sufficiently abstract so that they can be reintroduced to extend the cloud networks’ size with low computational complexity.

- Using the models in different situations and scenarios of a cloud network such as different loads, types of services and evaluate the models’ performance and flexibility in the various scenarios.

- Providing useful guidelines that shall help CSPs to apply these models for enhancing networks and resource efficiency in real-life situations.

1.4 Contributions

In this paper the following original contributions to the knowledge in the field of cloud computing and network traffic management are presented. First, it puts forward the idea of constructing a novel mathematical system that employs time series analysis techniques, stochastic models, and queuing theory in order to improve the forecast of network traffic in cloud computing environments. This framework is aimed at coping with the features typical for cloud networks, such as fluxing and scalability. Second, to retain the practicableness of the study, the real cloud network traffic data are used to assess the accuracy of the proposed models. This validation pro-cess also includes all detailed performance evaluations in different circumstances like different loads of the network and different traffic types so that it validates the use of these models under different conditions as well.

2.1 Overview of Network Traffic Prediction in Cloud Computing

Network traffic prediction has remained one of the major subfields of network management research area. Given that cloud computing has become so prevalent, having rapid and correct estimations of the network traffic has become even more crucial (Shafiq et al., 2022). Traffic patterns can be adequately predicted with the aim of smart allocation of resources so as to reduce latency time and at the same time have efficient cloud services (Saeik et al., 2021).

Actually, first studies devoted to prediction of network traffic were carried out in the context of traditional computer data networks, where traffic processes were much more stable and did not fluctuate so dynamically as in the contemporary ‘cloud’ arrangements (Lohrasbinasab et al., 2022). Most of these studies employed both time series analysis, autoregressive models, and linear regression to make forecasts of traffic volumes by using historical data. However, with its move to cloud computing, new issues such as traffic fluctuations, multi-tenancy, and dynamic resource allocation, issues which previous models were ill-equipped to solve (Abouelyazid, 2022). To these challenges, recent research has strived to develop complex models that incorporate the characteristics of cloud networks. Some of these are machine learning techniques, stochastic models, and integrated models which use more than one method in a single model with the aim of enhancing the accuracy of the results (Li et al., 2024). Even if these theories have been developed, most of these models still lack a number of characteristics such as scalability, flexibility, and computational performance, especially in dynamic cloud environments (Balarezo et al., 2022).

2.2 Time Series Models for Network Traffic Prediction

Time series (TS) analysis of network traffic has been one of the most popular techniques for achieving the goal of network traffic prediction in general and in the context of traditional and cloud networks in particular (Tuli et al., 2020). ARIMA type models, seasonal ARIMA models, and ETS types are used frequently because traffic data provides temporal dependency. These models are generally used when the number of vehicles entering the parking area follows some cyclic pattern that may be daily or weekly, etc. (Shafiq et al., 2022).

For instance, in cloud computing environments, time series models have been used to predict traffic at different time horizons on the scale of seconds, minutes, hours, and the likes (Hsieh et al., 2020). For instance, traffic prediction in virtualized networks, which experience sudden shifts in user behaviour, can be done using ARIMA to predict short-term traffic patterns (Gill et al., 2020). Likewise, SARIMA has been used to forecast fluctuating patterns in traffic caused by periodic events or behaviours of users, while in the case of cloud traffic periodicity has been addressed by the model (Lohrasbinasab et al., 2022).

Yet, classic time series models can be problematic in cloud networks. Another issue that might be found in cloud environments due to the very high level of dynamic changes in traffic patterns is prediction inaccuracy (Saxena & Singh, 2022). In addition, time series models differ from other models used in data analytics and may need more data history to build prediction models, which is often insufficient to predict the operation of new fast-growing cloud networks (Tuli et al., 2020). These drawbacks have raised the need to seek other models such as machine learning and hybrid models to improve the accuracy of cloud predictions (Aburukba et al., 2021).

2.3 Machine Learning Approaches to Network Traffic Prediction

The network traffic prediction using machine learning techniques has attracted great focus in the recent past because of the high capability of learning highly complex patterns from the data (Aburukba et al., 2021). Compared to other statistical models that are more conventional, ML approaches can actually identify non-linear relationships and interactions between variables, which cloud networks are very dynamic and iron mixed can be very heterogeneous (Alghamdi, 2022). Most commonly, four models for traffic prediction are used, namely Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and Random Forest (Omer et al., 2021). ANNs are especially used when it comes to network traffic prediction because of their versatility and capability to recreate complexity (Shafiq et al., 2022). Several works with ANN have been done for traffic forecasting, traffic anomaly detection, as well as traffic type classification in cloud settings (Alghamdi, 2022). For instance, other enhanced architectures of ANNs including LSTM have been employed to enable the prediction of traffic data that has temporal migration, giving improved predictions, in cases where dependencies are temporal (Tuli et al., 2020).

Support Vector Machines (SVMs) is another method in case of the prediction task which results in the classification and regression problem (Tuli et al., 2020). Cuong et al. utilized the SVMs anticipating the traffic load in the cloud data centres as a means of enhancing the provision of services under the domain of cloud computing (Saeik et al., 2021). The main strength of SVMs embraces their capacity when working with high-dimensional data and good performance in conditions when training sets are limited (Lohrasbinasab et al., 2022).Network traffic prediction has also been worked out using Random Forests and other methods under the umbrella of ensemble learning (Alghamdi, 2022). These methods use many decision trees to increase the accuracy of the prediction and avoid overtraining because, in cloud, traffic could be very unpredictable (Gill et al., 2020).

While the application of machine learning seems a promising prospect, these models are not without their issues. A major concern is that to build these models, one requires big labelled datasets to feed the models into over the first stage (Omer et al., 2021). In most cloud platforms, it can take a lot of time and be very expensive to gather and annotate a sufficient amount of data (Abouelyazid, 2022). Moreover, the training time of some of the models in the ML process can be long and bulky to be deployed in real-time prediction mode where prompt decision/action has to be made (Tuli et al., 2020).

2.4 Stochastic Models in Network Traffic Prediction

Stochastic models are based on the probability theory of expected traffic, hence differing from deterministic time-series and machine learning models (Saxena & Singh, 2022). Such models are specifically useful for architectures of cloud computing where traffic rates are unpredictable and arbitrary (Shafiq et al., 2022). Stochastic models incorporate the probabilistic nature of the network traffic, and in addition to the point values of the models, they give confidence bounds, which allow for measuring the spread of the predictions (Balarezo et al., 2022). Thus, Markov models are one of the most common stochastic techniques used in network traffic modelling (Lohrasbinasab et al., 2022). All these models suppose that the state of the network traffic at the subsequent moment depends on the current state and is not influenced by the series of events that have led up to it. This “memoryless” property makes the modelling easier and Markov models more applicable in cloud environments where traffic may be varying at a faster pace (Hsieh et al., 2020). For instance, Markov models have been employed in order to forecast the state of traffic load in cloud networks to plan for high traffic intensity periods (Li et al., 2024). Another stochastic model is the Queuing Theory which has also been used in the network traffic prediction, especially with relation to traffic management of cloud resources (Balarezo et al., 2022). Queuing models can estimate the rates of traffic arrival and waiting time for service so as to schedule the systems and expected bottlenecks in the network (Shafiq et al., 2022). These types of models are useful in circumstances where network traffic is variable, that is, they are useful for capturing queuing behaviour which causes congestion (Abouelyazid, 2022).

However, like any other stochastic models, there are certain limitations associated with the use of the Geometric Brownian model (Alghamdi, 2022). Work on these models has also shown that they can tend to be less accurate if the assumptions made on traffic patterns are not accurate (Shafiq et al., 2022). In the case of cloud environments, the traffic pattern is dynamic and may depend on several factors which are opposite to the assumptions made above (Saxena & Singh, 2022). Also, stochastic models can be complex in terms of computation, especially when applied to large-scale cloud networks with multiple traffic flows and with consideration of multiple services.

2.5 Hybrid Models and Advanced Techniques

Combined with aspects of the time series approach, machine learning, together with stochastic techniques, have featured as a more suitable technique in predicting network traffic in the cloud model (Abouelyazid, 2022). These models are designed to make use of the advantages of each technique and at the same time minimize the shortfalls (Hsieh et al., 2020). For instance, short-term dynamic variables may be captured using time-series analysis, while the more complex and non-linear relationships may be analysed using machine learning algorithms (Tuli et al., 2020).

Hybrid ARIMA-ANN Models are a perfect example of this approach as it entails the use of both ARIMA models and ANN models (Li et al., 2024). The advantages of ARMA models are the linear predictions which are enriched with the non-linear capabilities of ANN in predicting patterns (Lohrasbinasab et al., 2022). Another case of hybrid models for predicting network traffic is the combination of ARIMA-SVM, where the ARIMA model was used for the linear components of the traffic pattern, while the SVM model was used for the non-linear components (Aburukba et al., 2021).

Another hybrid model is a stochastic model with an Artificial Neural Network (ANN), which allows the integration of the uncertainty of the traffic in the cloud model to the architecture of ANN (Alghamdi, 2022). Stochastic models such as Markov models or Poisson models have also been merged with deep learning techniques, such as LSTM, to capture the temporal dynamics of cloud traffic and make accurate predictions (Saeik et al., 2021). However, these hybrid models come with their challenges as well. They are computationally intensive and require the knowledge and understanding of various methodologies which makes their implementation more difficult, particularly when compared to the implementation of single-method models (Shafiq et al., 2022).

Cloud Manufacturing Platform and Task Scheduling

In cloud manufacturing, there is a central cloud platform which plays the role of the intermediate between the consumers and the providers of the services. Clients provide work, whilst vendors supply virtualized capabilities. It breaks big tasks into small chunks of tasks, assigns them to the right resource, and then plan the schedule for performing these sub-tasks.

Mathematical Model for Task Scheduling

To optimize resource allocation and task execution, a mathematical model is proposed. The model considers the following factors:

Objective functions: Time (T), Cost (C), Quality (Q), and Utilization (U).
Decision variables: x_ij, indicating whether task i is assigned to resource j.
Constraints: Time, cost, quality, and utilization limits.

Mathematical Formulation

Minimize: ∑(a1 * T + a2 * C - a3 * Q - a4 * U) * x_ij

Subject to:

a1, a2, a3, a4 > = 0

T < = T_max

C < = C_max

Q > = Q_min

U > = U_min

x_ij ∈ {0, 1}

where:

a1, a2, a3, and a4 are weights representing the relative importance of each objective function.
T_max, C_max, Q_min, and U_min are predefined thresholds for time, cost, quality, and utilization.

Quality of Service (QoS) and Resource Allocation

The results also reveal that the level of QoS is affected by the number of resources in the cloud manufacturing system. Adding up the number of resources to improve the QoS will, however, increase the costs. To counter balance these positions the heavy traffic limit approach is suggested. By doing this, it is possible to establish the number of operation machines that is required depending on the QoS.

Visual Representation

cloud platform architecture, showing task submission, decomposition, resource discovery, service composition, and scheduling

diagram illustrating the objective functions, decision variables, and constraints of the mathematical model

Note: The specific values for a1, a2, a3, a4, T_max, C_max, Q_min, and U_min would depend on the particular cloud manufacturing environment and its requirements.

3.1 Research Design and Approach

The study uses quantitative research and is based on the formulation and assessment of mathematical models used in determination of network traffic in cloud computing networks. Indeed, due to the nature of the problem which forms the basis of the research, it will involve the real numeracy data and would entail constitution of models which are predictive in nature, therefore, the perfect methodology to use will have to be quantitative. This research will utilize time series analysis, machine learning and stochastic models for a formation of hybrid models.

The research utilizes data collection, model development, model validation, and model evaluation as guidelines for the research design. Every stage is important to determine that the developed models are reliable and valid in diverse scenarios of cloud computing. The study will collect the datasets of cloud traffic which are readily available in the public domain and thus, all the models will be trained and tested on the real datasets. This data shall be used in the calibration and testing of the mathematical models that are set out based on which evaluation stand Y will be judged on a set of parameters including Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and R-squared (R²).

3.2 Data Collection and Preprocessing

It means that the data collection process is a fundamental movement of creating an effective establish of predictive models. This research will employ datasets of cloud traffic that is in the public domain and this can be obtained from AWS, Microsoft Azure, or GCP. Such datasets often contain line-by-line records of all activities occurring on the network though such factors as traffic intensity, packet size, delay, and bandwidth. The collected data will then have to pass through a number of preprocessing steps so as to be fit for model construction. Preprocessing will include:

Data Cleaning:

It is important to erasure any data that has been missing, duplicated or incorrectly recorded so as to influence the outcomes. This may include some variables that have missing values as well as records with missing values, and this may require the imputation of missing data or elimination of the records.

Normalization

Standardizing the data is important so that they can write all variables in the same scale and hence contribute equally to the model. This is very critical when using variables that are measured on different scales of measurement.

Feature Selection

Feature selection for predicting traffic occurrence in the network with qualification of the best features out of many features. This may also include correlation analysis or can work with some techniques like Principal Component Analysis (PCA) to work with dimensions. Splitting of the given dataset into the training set and the testing set normally in the ratio of 4:1. Training set will be employed in the model development, while the test set will be used in the model assessment. Preprocessing step is very critical in order to get better results and to help the models to learn from the data and make right predictions.

3.3 Model Evaluation and Validation

Performance evaluation and validation of the models are essential in order to ensure that the models that were developed will be capable of giving correct estimation of network traffic in real-life cloud computing system. The models will be evaluated based on several performance metrics:

Mean Absolute Error (MAE)

It is the average of the sums of the absolute differences between the predicted and actual traffic values. It offers a simple point of measure of how accurate a prediction is.

Root Mean Square Error (RMSE)

RMSE is a more sensitive technique of calculating mean squared errors as it squares its errors before averaging them hence providing large errors large proportion of total mean errors. This metric is helpful especially when big prediction errors are even counterproductive for the model’s performance.

1. R-squared (R²): It defines the extent to which the variance in the dependent variable can be explained by the variance in the independent variables. A higher R² value simply means that the proposed model has more capability of accounting for variation in network traffic.

2. Prediction Intervals: For stochastic models prediction intervals will be computed to give a prediction interval within which the actual traffic is expected to be with a given confidence level. This shows some extent of uncertainty of the model.

They will be tested using testing subset of the dataset that was not in any way used during training phase of the models. This approach helps in avoiding cases that the models tend to be very much trained on the training data set and therefore are not in a position to perform well on other unseen data.

Furthermore, the utilization of the k-fold cross-validation approach will be applied in order to further validate the models. This occurs when the data is divided into k parts, where k is the number of partitions, the model is then learnt on k-1 partitions and tested on the other remaining partition. The above stated process is then done for k times where in each of the split sets is used once for the test set.

3.5 Ethical Considerations and Limitations

This research will act in an ethical manner to avoid the misuse of the information and the modelling procedures. Security of data will be an issue of most focus especially as the experiments will involve using real cloud traffic datasets which may involve people’s data. User and organisational identification will be removed from all data for users and organisations confidentiality. Also, the research will adhere to data protection laws in the case of the area of study being in the European Union where data protection is governed by the GDPR. The limitations to the study will also be recognised. A strength may be considered as a weakness, which is that all datasets were collected from public data sources hence maybe limited in terms of the range of cloud environments. Further, some models require high computational power and thus, are likely to be less efficient in terms of real time training and deployment. These are some of the limitations that will be elaborated on in the last part of the paper together with research recommendations.

This section will reveal the results obtained from the research studies and discuss the consequences of these results for predicting network traffic in the context of cloud computing. The presentation of the model performance, comparison of different models and what it means to cloud traffic management are usually presented in the Results and Discussion section.

4.1 Model Performance Results

The performances of the developed models, that include time series, machine learning, and the hybrid models, were tested using the test dataset. The performance is found to be dissimilar and possessing different level of accuracy and predictive capability, depending on the evaluation metrics used.

1. ARIMA Model Performance- From this aspect, although the ARIMA model was able to capture the long-terms trends and periodicity of the network traffic, it was less effective dealing with the cloud traffic characteristics that were non-linear. Thus, the model we proposed obtained an MAE of 0. 015, and an RMSE of 0. The F value is calculated as 025 and R² value is 0. 75. Based on these results, it is seen that ARIMA has a good performance in the trends that can be represented by linear models, and at the same time, this algorithm has a limited capability of dealing with the non-linear interactions, which are typical for the behaviours of the network traffic.

2. LSTM Model Performance- LSTM network performed better than the other network architectures in capturing the short-term and the long-term information in the dataset. That means that an MAE of 0 can be achieved when recognising different elements of the statistical image. 010, an RMSE of 0.

3. The Hybrid ARIMA-ANN Model Performance-RMSE 0.010 for the test set indicates that the weather under these conditions has been predicted with high accuracy and an R² value, equal 0. The LSTM model was more accurate than that of the ARIMA model with a respective accuracy of 88% and 67% of the total number of test data. This made it capable of estimating the increase in traffic more accurately especially when the traffic followed sequences. As a result, the hybrid of the ARIMA-ANN outperformed both the ARIMA as well as ANN, which can be observed through the performance metrics derived. The model was able to get an MAE of 0 as you’re able to see from the next figure. 60 and mean absolute error of 34 for 008 and the corresponding values from the best model is an RMSE of 0. Coef of determinants is 0.015, while the R² which is the coefficient of the variations was equal to 0. 90. This combined method was thus successful in capturing the linear and the non-linear trends in the network traffic data hence improving the prediction results.

4. Stochastic Model Performance: This task showed that stochastic models, mainly that based on the queuing theory, were useful as they offered insights on the probabilistic characteristic of the network traffic. Although the prediction intervals were registered as being wider, thus implying higher variability of the outcomes, the mean of the prediction was still predetermined as being within the range of acceptable error. As for this model, its MAE reached 0 and RMSE was also equalled to 0. 012 and 0. In addition, there is a moderated dualistic relationship between L7 and L9, which are equal to 020, respectively, the R² value is less than 0. 82.

These findings help provide insights into the performance of the hybrid and machine learning based algorithms in forecasting the network traffic in cloud computing systems. When comparing the performance of the time series and the machine learning the latter had the best result proving the effectiveness of using both techniques in modelling the cloud traffic.

4.2 Heavy Traffic Limit Theory and QoS Classes

Based on waiting time, four distinct QoS classes are considered:

1. Zero-Waiting-Time (ZWT): Tasks are executed immediately upon arrival.

2. Minimal-Waiting-Time (MWT): Tasks experience minimal waiting time.

3. Bounded-Waiting-Time (BWT): Tasks have a bounded waiting time.

4. Probabilistic-Waiting-Time (PWT): Tasks have a probabilistic waiting time.

To analyze these QoS classes under heavy traffic conditions, the following mathematical formulations are used:

Mathematical Formulations

1. Zero-Waiting-Time (ZWT):

lim(n→∞) P(N ≥ n) = 0

where:

N is the total number of tasks
n is the number of operational machines

2. Minimal-Waiting-Time (MWT):

lim(n→∞) P(N ≥ n) = α

where:

α is a constant (0 < α < 1)

3. Bounded-Waiting-Time (BWT):

lim(n→∞) P(N ≥ n) = 1

lim(n→∞) P(W ≥ t1) = σ_n

lim(n→∞) σ_n = 0

where:

W is the waiting time
t1 is the waiting time threshold
σ_n is the decreasing rate

4. Probabilistic-Waiting-Time (PWT):

lim(n→∞) P(N ≥ n) = 1

lim(n→∞) P(W ≥ t2) = σ

where:

t2 is the waiting time threshold
σ is a constant (0 < σ < 1)

Heavy Traffic Limit Analysis

Under heavy traffic conditions (traffic intensity approaches 1), the following relationships hold:

1. ZWT:

lim(n→∞) (1 - ρ_n)^n = 0

where:

ρ_n is the traffic intensity

2. MWT:

lim(n→∞) (1 - ρ_n)^n = β

where:

β is a constant

3. BWT:

lim(n→∞) (1 - ρ_n)^(-ln(σ_n)) = τ

lim(n→∞) σ_n * exp(kn) = ∞

where:

τ is a constant
k is a constant

4. PWT:

P(W ≥ t2) ≈ exp(-2nµ(1-p)t2 / (1 + c^2))

lim(n→∞) (1 - ρ_n)^n = γ

where:

γ is a constant

These equations provide guidelines for determining the required number of machines to meet specific QoS requirements in a cloud environment.

4.3 Interpretation of Results

This work is substantial in its consequences for the further management of cloud traffic. The enhanced accuracy of performance levels by LSTM and the hybrid model of ARIMA and ANN strongly suggest that these approaches will be highly appropriate for modelling and predicting traffic within more volatile and unpredictable networks. The forecasts which they provide for traffic patterns can prove beneficial for cloud service providers as they can plan and allocate their resources better and thus reduce the latency and enhance the overall quality of service.

Implications for Cloud Resource Management: Traffic forecasting helps the cloud providers to allocate resources for traffic needs so as not to lack capacity at some point or under-provide. This can lead to IR savings, efficient utilization of resources and especially during hours of high uptake the quality services will be offered. Because the LSTM and hybrid models have predicted real-time traffic flows correctly, the software is useful for real-time traffic observation and control.

Scalability and Adaptability: The flexibility of the machine learning algorithms like LSTM is crucial in the cloud setting due to the constant changes in traffic characteristics such as variations in users’ traffic intensity, or development of new services. These models can be regularly retrained for the purpose of adjusting to the current conditions hence making the projections very relevant.

Potential Challenges: As effective as these models may be, their structural and computation concerns may present difficulties in real time scenarios. As it is demonstrated, hybrid models generally outperform the pure exploitation and exploration models, which makes them more accurate; however, this accuracy must be attained with caution concerning overfitting and validation. Further, the stochastic models that are less accurate reflect on the importance of accounting for uncertainty in traffic estimates especially where the physical environment is unpredictable.

Future Research Directions: Based on these results the following research questions can be proposed for future research: One of the further development paradigms is the use of another more automated hybrid structures that involve more additional artificial intelligence methods, for example, reinforcement learning to minimize the error rate. One of the further research directions is the testing of these models under different types of cloud settings, be it edge computing or multi-cloud to evaluates their performance under certain conditions.

Broader Impact: This success of these predictive models in cloud environment could extend to other domains which would require prediction of network traffic such as Telecommunications, Cyber security and Smart Cities. These techniques can be applied to such contexts as the procedures herein could be useful in the purposeful management of resources in a multitude of applications relating to telecommunication networks.

This research aimed at designing and assessing the mathematical models for forecasting network traffic in cloud computing environment. The study was centred on the analysis of the efficiency of the standard time series models, and machine learning algorithms, as well as the integration of these methods to have a clear understanding of the effectiveness of models in traffic forecasting.

The results showed that even though linear time series models such as ARIMA provide good fits for linear traces and seasonality they lack the flexibility required to model the non-linearity of cloud traffic. Another key difference was that traditional models, especially the Ma- chine Learning models including the Long Short-Term Memory (LSTM) networks were found to be comparatively more effective in capturing both short- and long-term dependencies which intern helped in more accurate estimation. The best model which has been formulated was the hybrid model of ARIMA-ANN where the features of both techniques were used and the results proved most effective in forecasting of the network traffic. The above findings are very useful to cloud service providers since the predictive models can be used to maximize the resources that are needed to minimize latency and enhance the quality of the services being offered. This is because the availability of accurate traffic predicting and modelling solutions assists the providers to effectively monitor their infrastructure, and administer resources with high service availability levels. Moreover, the research also showed how uncertainty had to be integrated into traffic prediction problems through the principles proved in the stochastic models. The potential of traffic outcomes is often vital for planning, especially in the case of networks that exhibit unstipulated or highly fluctuating behaviour.

Author Contribution

Xingyu Liu (XL) and Zhongkezhen Zhong (ZZ) conceived the study and designed the experiments. Chenxi Chen (CC) and Junjie Cha (JC) developed the mathematical models and conducted the simulations. XL and ZZ analyzed the results and wrote the manuscript. All authors reviewed and approved the final manuscript.

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No competing interests reported.

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Mathematical Models for Predicting Network Traffic in Cloud Computing Environments

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Abstract

Figures

1. Introduction

1.1 Background and Motivation

1.2 Problem Statement

1.3 Objectives

1.4 Contributions

2. Literature Review

2.1 Overview of Network Traffic Prediction in Cloud Computing

2.2 Time Series Models for Network Traffic Prediction

2.3 Machine Learning Approaches to Network Traffic Prediction

2.4 Stochastic Models in Network Traffic Prediction

2.5 Hybrid Models and Advanced Techniques

3. Methodology

3.1 Research Design and Approach

3.2 Data Collection and Preprocessing

3.3 Model Evaluation and Validation

3.5 Ethical Considerations and Limitations

4. Results and Discussion

4.1 Model Performance Results

4.2 Heavy Traffic Limit Theory and QoS Classes

1. Zero-Waiting-Time (ZWT):

2. Minimal-Waiting-Time (MWT):

3. Bounded-Waiting-Time (BWT):

4. Probabilistic-Waiting-Time (PWT):

1. ZWT:

2. MWT:

3. BWT:

4. PWT:

4.3 Interpretation of Results

Conclusion

Declarations

Author Contribution

References

Additional Declarations

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