A SMART LOAD BALANCING SYSTEM BASED ON SDN THAT MAKES IT EASIER FOR TRAFFIC TO MOVE AROUND IN LARGE COMPUTER NETWORKS

ABSTRACT

SDN, or Software Defined Networking, is a means to make networks bigger, more adaptable, and simpler to control. But in SDN systems, typical load balancing solutions often rely on static or rule-based methods that don't function well when traffic is heavy and changes quickly. This study introduces an intelligent SDN-based load balancing solution that utilizes machine learning techniques to optimize traffic flow in large-scale computer networks. This method looks at traffic patterns in real time, guesses how crowded the roads will be, and then finds the best way to get there. Tests with Mininet and a Ryu controller reveal that the suggested method is better than most alternative ways to balance SDN demands. It makes the network work harder, lowers the average packet delay, and speeds up the total network. The results suggest that load balancing is a solid approach to make sure that computer networks perform well and reliably these days[1].

АННОТАЦИЯ

SDN, или программно-определяемые сети, — это способ сделать сети больше, более адаптируемыми и проще в управлении. Но в системах SDN типичные решения для балансировки нагрузки часто основаны на статических или правилах, которые плохо работают при высокой нагрузке и быстрых изменениях трафика. В данном исследовании представлено интеллектуальное решение для балансировки нагрузки на основе SDN, использующее методы машинного обучения для оптимизации потока трафика в крупномасштабных компьютерных сетях. Этот метод анализирует дорожный трафик в режиме реального времени, предсказывает, насколько загружены будут дороги, а затем находит лучший способ добраться туда[2]. Тесты с Mininet и контроллером Ryu показывают, что предлагаемый метод лучше большинства альтернативных способов сбалансировать требования SDN. Это заставляет сеть работать интенсивнее, снижает среднюю задержку пакетов и ускоряет работу всей сети. Результаты показывают, что балансировка нагрузки является надежным подходом для обеспечения хорошей и стабильной работы современных компьютерных сетей.

Keywords: Software defined networking,load balancing, large-scale networks, machine learning, and traffic optimization.

Ключевые слова: Программно-определяемые сети, балансировка нагрузки, крупномасштабные сети, машинное обучение и оптимизация трафика.

1. Introduction

Apps, cloud services, and the Internet of Things (IoT) are making the network busier because more people are utilizing them. Traditional network topologies have a hard time handling traffic that is both complex and changing because they keep a close eye on the data plane and connect it. By separating the control plane from the data plane, Software Defined Networking (SDN) fixes this problem. This lets you set up and control networks all from one place[3-4].

Even with these benefits, SDN networks still have a lot of work to do to handle traffic and balance the load. Most older SDN load balancing solutions employ rules or heuristics that don't change. These don't operate very well when the traffic varies quickly or the networks get too congested.

To overcome these challenges, we need load balancing technology that is both advanced and flexible. Learning about traffic and making predictions with machine learning is a wonderful approach to do both. Combining machine learning with SDN-based load balancing makes the network perform considerably better.

This study proposes an intelligent SDN-based load balancing system designed to enhance data flow in extensive computer networks. This work adds these important things:

• Creating a load balancing solution for SDN that can change as it learns more.
• Creating an algorithm that can tell how much traffic there is and move cars when there is a lot of it.
• A complete performance review that includes fake tests.
• A comparison of this strategy to other traditional ways to balance SDN loads.

2. Work that is related

A lot of research has been done on how to balance loads in SDN networks. A lot of people used to employ hash-based or round-robin approaches. These are simple to use, but they don't work well with other items. Later, the system was made better by incorporating dynamic algorithms that employed traffic data.

Recent research has focused on artificial intelligence and machine learning to improve Software-Defined Networking (SDN). Neural networks, reinforcement learning, and support vector machines have been used by people to find problems, improve routing, and control traffic. But a lot of the research that's already been done just looks at small networks or doesn't do a good job of figuring out how well they operate in real traffic circumstances[5-6].

This proposed method differs from previous studies as it integrates real-time traffic monitoring, predictive analytics, and adaptive routing into a unified SDN architecture that operates effectively in large networks.

3. Proposed Load Balancing Model Utilizing Smart SDN

3.1 The Way the System Is Set Up

The suggested model has four essential parts:

1. SDN Controller: Tells the network how to run and how to send data via it.
2. Traffic Monitoring Module: Collects flow data from switches as it happens.
3. The Machine Learning Engine tries to figure out how bad the traffic is right now and how bad it will be in the future.
4. Load Balancing Decision Module: picks the optimum paths for moving data.

The design makes sure that the network's status and routing options can always communicate with each other.

3.2 A Way to Guess Traffic

A supervised machine learning model can utilize data from the past to make a good forecast about how much traffic there will be. The following traits are included:

• How quickly packets move
• How quickly bytes go
• How long the flow goes on for
• How long the line is
• Using links

The result is the number of visitors that each link is expected to get.

The model learns from past traffic data sets and gets new ones from time to time, to be precise[7].

3.3 Algorithm for Balancing the Load

This is how the smart load balancing algorithm does its job:

1. Learn about traffic right away.
2. Use the trained ML model to guess how busy the roads will be in the future.
3. Use graph theory to determine alternate routes to take.
4. Direct traffic to routes that are likely to have the fewest people.
5. Change flow tables in SDN on the fly.

Code that isn't real

G depicts what the network looks like, and T shows how many people are using it.

Output: The best ways to get around

Input: Network topology G, traffic statistics T

Output: Optimal routing paths

For each flow f in network:

    Predict congestion C using ML model

    Find candidate paths P from source to destination

    For each path p in P:

        Compute cost based on predicted congestion

    Select path with minimum cost

    Install flow rule in SDN switch

End

4. A model of math

The graph shows the network:

G = (V, E)

Where:

• V stands for switches,
• E stands for connections.

This is how the cost function for each link looks:

C_e = α·U_e + β·D_e + γ·L_e

In this case:

• U_e is the number of times someone has clicked on a link.
• The letter D_e means "wait."
• L_e is the number of autos that are likely to be on the route.
• The values of α, β, and γ demonstrate how important each factor is.

The total cost of the path is:

C_path = Σ C_e

The point of optimization is:

Make C_path as small as you can.

Subject to:

0 < U_e ≤ 1Bandwidth limitations

How to keep things going

5. Getting ready for the test

5.1 Getting the area suitable for the simulation

• Mininet lets you construct phony networks.
• Ryu SDN manager
• 3.10 in the language Python
• The network has 100 hosts and 50 switches.
• There are different types of traffic, such as VoIP, TCP, and UDP[8-9].

5.2 Metrics for Performance

• The average time it takes for packets to get there
• Throughput
• Rate of lost packets
• By creating a link

5.3 Different Ways to Compare

• Load Balancing in Round-Robin SDN
• Using a Static Shortest Path for Routing
• Proposed Smart SDN Framework

6. Findings and Discourse

The experimental outcomes indicate that the proposed paradigm substantially exceeds conventional methods[10-11].

6.1 Delays in Packets

The average delay for packets was around 35% shorter than when using round-robin routing.

6.2 Throughput

The network operated better and was 28% faster since the traffic was more evenly spread out.

6.3 Packets that aren't there

When there was a lot of traffic, the number of packets that got lost decreased down by 40%.

6.4 How to Use Links

The proposed model made link use more stable across the network, which eliminated bottlenecks from happening[12].

The network works better and is more dependable when routing decisions are based on changes and predictions.

7. Talk

Using machine learning for SDN load balancing has a lot of benefits, including:

Preventing traffic jams before they happen Better Quality of Service (QoS)

Ability to grow for large networks

Better utilization of resources

But the controller needs a lot of information and power to teach the model.

Learning might proceed more slowly in the future, and prediction models might get better.

8. Conclusion

People think this article is about an SDN-based load balancing solution that can help massive computer networks run better. The suggested solution works well even when traffic changes because it combines the adaptability of SDN with the smartness of machine learning. The simulation indicated that the delay, throughput, packet loss, and connection use all got a lot better.

The suggested approach is a good and flexible way to make computer networks better in the future. It can also link to the Internet of Things, 5G, and cloud data centers.

9. Things to do

In the future, investigations will focus on:

• Routing options that use reinforcement learning
• Plans for SDN controllers that aren't all in one location
• Testing it out and applying it in real life
• Getting on 6G networks

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