Real-Time Network Topology Optimization Using Dynamic Machine Learning Adaptation

Abstract

The environmental complexities of evolving network infrastructures require adaptively efficient and smart optimization of topologies in network environments. At present, the changing requirements of various networks within applications with real-time dynamic inputs fail to adapt in such circumstances that involve changes to data traffic, user demands, and environmental factors. Traditionally, static topologies fail to cope with the same. This paper establishes a new framework called Dynamic Network Topology Optimization (DNTO), which is powered by advanced machine learning algorithms, to optimize network configurations autonomously. The proposed framework integrates reinforcement learning to enable dynamic topology adjustments through learning from evolving traffic patterns to improve adaptability and resilience in network infrastructures. It optimizes multiple KPIs concerning latency, throughput, and energy efficiency and minimizes intervention and downtime. This efficacy of the proposed framework is further presented with effectively improved performance and adaptability over the static topology optimization methods, through experiments run on simulated and actual network scenarios. Scalable and intelligent solution for evolving networks constitutes this work and lays down an important foundation for real-time autonomous management of networks in areas of IoT, 5G, and cyber-physical systems.

Introduction

I. INTRODUCTION

As IoT, 5G, and cyber-physical systems start integrating into the infrastructures, the topologies of such networks become complex and fail to keep up with dynamic arrival patterns of traffic and usage patterns. Therefore, it is not only necessary to have adaptive topologies with network configurability but also to change along with the dynamic environments themselves. ML was a transformative tool in network optimization with powerful predictive and adaptability features that can make real-time adaption without any human intervention. This research comes up with a new framework for dynamically optimizing network topologies using reinforcement learning in response to data patterns and user demands. In this solution, optimal performance is guaranteed through continuous adaptation of topology according to evolving traffic, thus minimizing latency and energy consumption while making it more resilient and adaptive. Such systems can be applied to diverse contexts such as IoT networks, smart city infrastructure, and advanced cyber-physical systems where network requirements fluctuate very significantly. Dynamic adaptation based on machine learning enables networks to be robust and flexible, hence foundational to future autonomous and resilient networks.

The primary objectives of this research are:

Develop a machine learning framework to sense when it is necessary to dynamically change the topology of a network based on real-time traffic and usage patterns in use.

To automatically learn the change of network topology configurations by adapting reinforcement learning to autonomously adjust the latter for maximizing flexibility and reducing human intervention.

Evaluating and optimizing latency, throughput, and energy efficiency metrics to ensure that the model provides better general performance of the network.

That will be confirmed through simulated as well as real-world network scenarios to validate the proposition for varied applications of IoT, 5G, and cyber-physical systems.

This solution must be scalable enough to support large, complex networks and yet robust under conditions of high variance in traffic and usage patterns.

II. LITERATURE REVIEW

A. Existing Studies Analysis

In classical user modeling techniques, the greater portion has relied on collaborative filtering, content filtering, and hybrid models, but as the behavior of users grows more complex, these methods manifest some limitations about catching dynamic changes and sparse data [6], [7]. Recommendation Algorithms are the core of the whole system. How to generate recommendation results depends on the user model. Deep learning-based recommendation algorithms have begun to replace the traditional approaches, since it performs better in handling big data and complex nonlinear relationships. Amongst the most common deep models is Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) that are capable of capturing latent user interest features quite well [8]. Context processing draws upon the users' information of the current context such as time, location, and device type, during the processing of a recommendation [9].

Topology optimization is a powerful engineering tool applied to optimize the distribution of material within a given design domain to achieve optimal performance under specified constraints [10, 11]. Traditionally, single-scale topology optimization focuses on determining the optimal layout of material within a structure at a single scale, directing attention mainly to maximize stiffness for a given set of loading conditions. But engineering applications require more than just structural efficiency [12]. Important attributes such as robustness of structure [13], high strength [14] and superior energy absorption [15], fluid circulation [16], thermal as well as acoustic insulation capacities [17] become highly important. Those attributes do not only enhance the generalized performance of a structure but also enable multifunctionality, without which any product cannot be really considered for any industry, no matter how small-from aerospace [18, 19] to biomedical [20, 21] and more [22].

TABLE I. Comparative Analysis of Existing Studies

B. Problem Statement

The rise of networked environments, including IoT systems, 5G networks, and cyber-physical systems, poses unparalleled flexibility and robust demands on the topology management of the network. The static topology approaches contribute nothing to adaptivity for temporary fluctuations in traffic or user demand as well as environmental conditions and deteriorate performance and increase latency and resource mal-allocation. Current solutions are either too rigid or demand large manual adjustments and thus fail to meet the requirements of modern, dynamic networks. This research addresses one of the major problems in this regard by developing a machine learning-driven framework for real-time autonomous network topology adaptation that, through reinforcement learning, continuously optimizes the network configuration, reduces latency and energy consumption, and increases resilience. This solution aims at creating a scalable, intelligent, and efficient dynamic network adaptation mechanism that is needed for applications in IoT, smart cities, and next-generation cyber-physical systems.

III. RESEARCH METHODOLOGY

A. Design of the Proposed Work

This research methodology of the paper is the design and implementation of a machine learning framework that uses reinforcement learning to produce real-time adaptability for adaptive network topology. Three phases are in focus in the research carried out: data collection and preprocessing, model training for dynamic adaptation, and continuous optimization of the network. Data collection and preprocessing: In this phase, data is collected from multiple sources of network traffic with varying loads, latency, and user-demand patterns. Data preprocessing: The collected data is corrected for inconsistent values missing data and anomalies so that good-quality inputs are fed into the machine learning model. Time series analysis is used to find patterns and trends, and this allows training for the reinforcement learning model in the prediction of network demands in a dynamic manner. This is the second phase: Model Training and Implementation of Reinforcement Learning Now a reinforcement learning model is created to dynamically adapt the topology configurations for the elements in the network. The network is treated as an environment where nodes (agents) interact with each other and learn optimal configurations in terms of certain reward functions that motivate efficient metrics such as low latency, high throughput, and minimum energy consumption. The RL agent iteratively explores various configurations by adjusting the network topology according to real-time feedback from performance.

Through repeated training episodes, the model refines its appreciation of the way that topological change to a network affects network performance and converges on an optimal configuration. In the final step, Continuous Network Optimization deploys the trained model into a real or simulated network environment; it continuously monitors network conditions and dynamically changes topology. When the data to be added to the model is available, it adjusts on the fly, always adapting to maintain the optimal configurations for fluctuating conditions. Apart from that, the model is self-update with a periodicity that brings in new data seeking to enhance its adaptability and responsiveness. In this process, the network is always optimized, thus minimizing manual intervention and supporting the strengthening of the well-being, resilience, and efficiency of the network in real-time.

Fig 1. Architecture Design for Dynamic Network Topology Optimization

The architecture in Figure.1 is crafted using a Design software. This proposed architecture diagram depicts an optimization system of dynamic network topology based on the reinforcement learning approach. This system allows for real-time configuration adjustments on networks for optimal performance under different conditions. A detailed explanation of each component and the role of that component in the architecture follows.

B. Data Collection and Pre-processing

The system starts by collecting data from several network sources, where this information encompasses latency, throughput, traffic load, and energy consumption about the state of a particular network. Collected data forms the basis upon which insight into the current state of the network is obtained. Data collected undergoes a pre-processing process where it becomes cleansed, normalized, and readied for use in defining the state of the network. This step ensures that the data is in a usable format and removes any noise or unmeaningful data that could interfere with accurate state definition.

C. Define State and Action Space

After this preprocessing, the system defines its understanding of the state of the network with regard to data. The state is a set of conditions and metrics related to the network in order to make decisions. This step is very important because the state implies the current configuration and status of the network, which the RL agent will use to decide on optimisation actions. It is the set of possible changes or actions that the RL agent can make on the network, which might consist of modification of routes, creation or deletion of nodes, or altering link configurations in reaction to real-time conditions.

D. Policy Optimization

The policy optimization component uses RL algorithms to determine the optimal policy. A policy, in this case, is defined as a strategy for the actions the agent needs to perform following the current state. Optimizing the policy focuses on maximizing the cumulative reward as it learns which actions drive the most desirable network performance over time.

E. Dynamic Network Topology Optimization Evaluation

Dynamic network topology optimization refers to continuous and automated re-optimization of the underlying network structure based on the policy learned by the RL agent. Therefore, this would be the one that would execute the selected actions to reconfigure the network topology with a dynamic response to changes in traffic loads, latency demands, etc.

F. Reward Function and Real-Time Adaptation

The RL agent is given feedback about the effectiveness of their actions through the reward function. The function calculates a reward in terms of key performance metrics, including decrease in latency, increase in throughput, and energy efficiency. Positive rewards are used to reinforce action that increases performance, while negative rewards discourage degrading performance. The reward function guides an agent's learning as it learns to identify useful actions to be reinforced. Real-time adaptation: The topology could be altered in real time to allow the system to adapt to changing conditions immediately. Based on the optimized policy, the RL agent observes a state of the network and hence makes appropriate decisions for adapting its topology. This will help the system stay responsive and agile in scenarios where the network condition keeps on changing.

G. Self-update model and Decision-Support

It ensures that the RL agent keeps improving its capabilities in decision-making within itself through the self-update model. The agent improves its understanding of the network and is better placed to manage complex, dynamic scenarios by making use of real-time performance data or even iterative updates for the policy. In this regard, actions by the RL agent and observed performance of the network offer insights and recommendations during decision support. This module can therefore provide the network administrator or operator with suggestions or feedback through which they can make an informed decision that is consistent with the automated adjustments based on the RL algorithm.

H. Proposed Algorithm

Algorithm 1: Algorithm for Dynamic Network Topology Optimization  

Let   represent the network topology graph, where   is the set of nodes and   is the set of edges.

1. Define the State:

The state  at time   includes the networks current topology and traffic metrics, given by:

1.  Action Space: Define a set of actions  , where each action   represents a potential adjustment to the topology (e.g. adding/removing edges, rerouting traffic).
2.  Reward Function: The reward   for an action   taken in state   is based on performance metrics such as Latency (L), throughput (T), and energy efficiency (E). For optimization we define the reward function as:

where  are weighting factors determined based on priority among metrics.  

1. Policy Optimization: The policy   represents the probability of selecting action   given state   The goal is to maximize the expected cumulative reward over time:

1. Learning Process:

• Value Function Update: Use temporal difference learning or a similar method to update the value function    
• Policy Update: Adjust the policy using gradient ascent to maximize the expected reward. For a parameterized policy   update   as follows:

where is learning rate.

1. Real-Time Adaptation: As new data is gathered, update   and continuously select the action   that maximizes the expected reward, refining topology configurations dynamically.
2. Terminate

I. Algorithm Analysis

The algorithm in use in this study is a version of Reinforcement Learning, such that it performs dynamic optimization of the network topology. It considers the network as an interactive environment within which the RL agent learns to change the configuration of the network toward optimal performance. In this case, the learning is done through continuous observation of network states, action selection, and reward-based learning. The algorithm starts with defining the space of states, which in this case can be described as the parameters of the network, such as latency, throughput, and energy consumption; besides, an action space is also implemented, comprising of possible network adjustments, as adding or removing links or changing the routes of the traffic. The RL agent receives feedback for taking these actions through the reward function, which was designed to increase throughput, reduce latency, and consume less energy. Several learning episodes of the training phase, with testing configurations, and receiving appropriate rewards as adjustments to topology progressively enhance the performance of the network, will enable the agent to learn with time to maximize the cumulative reward obtained by selecting a pattern of actions that will repeatedly return favorable performance outcomes. In this manner, it learns to adapt the network topology in real-time according to changing demands and conditions within the network. The agent continually refines its process for deciding so that optimal performance is ensured in a wide variety of scenarios, but through a dynamically adaptive network that self-adjusts based on real-time data.

IV. RESULTS & DISCUSSION

A. Experimental Results

The results have proved, which show that RL-based dynamic network topology optimization can significantly contribute toward the enhancement of key network performance metrics, such as latency, throughput, and energy consumption. The outcomes of simulated and actual real-world network scenarios are compared to the standard static topology optimization model.

TABLE II. Latency and Throughput Comparison

(a)

(b)

Fig 2. Comparison of (a) Latency and (b) Throughput

Average comparison of latency and throughput between the static configuration and the RL-based dynamic configurations is provided in Table 2. Experimental results have showcased a reduction of 57% in the latency achieved using the RL-based model that produced an average latency of 15 ms, compared to the static model that showed a latency of 35 ms. Such latency reduction has been a pre-requisite for extremely fast response times in real-time applications. Then, through the application of the RL-based model, throughput is improved by 30% as compared to static topology to reach a throughput of 650 Mbps, where only 500 Mbps existed in static topology. This increased throughput, therefore reflects high data transmission capability and the network is very efficient even in the case of heavy traffic loads.

TABLE III. Average Energy Consumption Comparison

Fig 3. Comparison of Average Energy Consumption

Table 3 Average energy consumption in average for both static and dynamic RL-based topologies. The energy consumption of the RL-based topology reduced by 25% compared to the static approach, showing 900 kWh for the former but 1200 kWh with the static approach. It improves the system's efficiency by showing that the model based on the RL is indeed energy-efficient because it will change the configuration so that there is a more optimal state that consumes less power. More important, energy consumption reduction is necessary, particularly in big network contexts, since it saves money and helps in environmental sustainability objectives.

TABLE IV. Response Time for Topology Adaptation

Fig 4. Response Time Comparison for Topology Adaptation

Table 4 shows the adaptation response times under different network conditions. Heavy traffic load, Low traffic load, Sudden traffic spikes; Fig. 6 It is demonstrated that the adopted dynamic topology adaptation by means of RL-based scheme was better than the static topology; the response times were better in all cases. For example, when the traffic load was heavy, the RL adapted in 2 seconds whereas in the static case, it took 10 seconds to adapt. Similarly, in the event of a sudden increase in traffic, the adaptation through an RL model was achieved in 3 seconds whereas 15 seconds for the static adaptation. This drastic reduction in the response time showcases the flexibility of an RL model when it readily responds to dynamic needs of networks and continues to operate with no interruption or degradation of performance under sudden or heavy loads.

B. Key Findings

Latency was reduced by 57%: This system reduced the latency factor by using the dynamic model based on RL, which can be favourable for user experience in real-time applications.

Higher Throughput: The proposed model achieves a better throughput of 30%, which suggests increased efficiency in data transmission under changing conditions.

Energy Efficiency: The model utilized 25% less energy, so it was sustainable for network management.

Fast response time: The RL model's snapshot-based adaptive capability makes its topological adjustments five times faster than that of its static counterpart, thus substantially enhancing the network's resilience.

C. Research Implications

Improved Network Stability: The rapid response to traffic fluctuations minimizes interference and ensures a relatively stable network performance.

Reduce Energy Consumption in Large Scale: Network management should be encouraged on energy efficiency that can support the environmental sustainability efforts. This is very important in smart cities and IoT systems.

Reduce operational costs. A machine adaptation minimizes the need for manual interference hence labor cost as well as direct the resources to innovation and expansion.

D. Limitations

Model Complexity and Training Time: The RL model needs to be trained on vast computational resources and time, making this model less feasible for real-time deployment in some environments.

Generalisability: Testing has been carried out only in simulated and selectively chosen real-world scenarios; the complexity and uniqueness of the network structures tend to be very high so the model still needs to be validated in such scenarios.

Dependence on Quality Data: The model depends on consistent and high-quality data for the purpose of training as well as adaptation. Any anomaly or noise in the data collection might hinder the functionality of the model.

Scalability to large networks: Although successful for smaller-scale networks, the scalability of this RL-based model in very large networks with very variable traffic patterns has yet to be explored.

Conclusion

In summary, the work presents how the dynamic capability of network topology optimization by using reinforcement learning can bring about a marked improvement in latency and throughput with respect to energy efficiency of the network. It surpasses traditional static topology configurations, and the developed RL-based model adapts faster to changes in network conditions and reduces intervention. This adaptability is very helpful for IoT, 5G, and cyber-physical systems whose network conditions change in rather unpredictable ways. Those results point to the potential in machine learning to design and deliver autonomous, real-time solutions for network use cases at the scale of next-generation infrastructures. A limitation relevant to the model complexity and the scalability of the proposed solution has been identified; yet, this work serves as a proper basis for future intelligent, energy-efficient network management advancement. The model can be further optimized so that it supports very large networks, and the limitations presented by this study may become areas for further improvements in later works.

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Copyright

Copyright © 2025 Riddhima Garg, Ritika Budhiraja, Akshita Gupta, Hardik Sharma, Suryagayatri Thangalazhi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.