Ai-Based Load Balancing In Cloud Networks Using Reinforcement Learning
Keywords:
Artificial Intelligence (AI), Reinforcement Learning (RL), Deep Q-Network (DQN), Cloud Computing, Load Balancing, Cloud NetworksAbstract
Efficient load balancing is a critical challenge in cloud networks due to dynamic workloads, heterogeneous resources, and fluctuating user demands. Traditional load balancing algorithms such as Round Robin, Least Connections, and heuristic-based approaches operate on predefined rules and often fail to adapt to real-time variations in network traffic and resource utilization. This paper presents a coding-based implementation of an AI-driven load balancing framework using Reinforcement Learning (RL) to achieve adaptive and intelligent traffic distribution in cloud environments.
The proposed system models the cloud network as a Markov Decision Process (MDP), where the load balancer acts as an agent that observes system states including CPU utilization, memory usage, queue length, and network latency across multiple virtual machines. Based on these states, the RL agent selects optimal actions for task allocation to maximize cumulative rewards defined in terms of reduced response time, balanced utilization, and minimized SLA violations. A Deep Q-Network (DQN) algorithm is implemented using Python and TensorFlow/PyTorch to enable scalable decision-making in large-scale distributed environments.
Experimental evaluation compares the RL-based load balancer with conventional algorithms under varying workload conditions. Results demonstrate significant improvements in throughput, reduced response time, better resource utilization, and enhanced system stability. The coding-based simulation validates the effectiveness of reinforcement learning in enabling autonomous, self-optimizing cloud load balancing. The proposed approach contributes to the development of intelligent cloud network management systems capable of real-time adaptation and continuous learning in dynamic environments.
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