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Reinforcement Learning-Based Adaptive Load Balancing for Dynamic Cloud Environments

Kavish Chawla

TL;DR

The paper tackles the challenge of dynamic cloud workloads where static load balancing underperforms. It introduces a Q-learning–based adaptive load balancing framework that continuously learns task distribution policies from real-time server metrics to minimize latency and balance resources. Experimental results in a CloudSim setting show the RL approach reduces response times, improves resource utilization, and increases task completion rates compared with traditional methods, especially under fluctuating workloads. This work demonstrates the potential of AI-driven load management to enhance the efficiency and scalability of modern cloud infrastructures.

Abstract

Efficient load balancing is crucial in cloud computing environments to ensure optimal resource utilization, minimize response times, and prevent server overload. Traditional load balancing algorithms, such as round-robin or least connections, are often static and unable to adapt to the dynamic and fluctuating nature of cloud workloads. In this paper, we propose a novel adaptive load balancing framework using Reinforcement Learning (RL) to address these challenges. The RL-based approach continuously learns and improves the distribution of tasks by observing real-time system performance and making decisions based on traffic patterns and resource availability. Our framework is designed to dynamically reallocate tasks to minimize latency and ensure balanced resource usage across servers. Experimental results show that the proposed RL-based load balancer outperforms traditional algorithms in terms of response time, resource utilization, and adaptability to changing workloads. These findings highlight the potential of AI-driven solutions for enhancing the efficiency and scalability of cloud infrastructures.

Reinforcement Learning-Based Adaptive Load Balancing for Dynamic Cloud Environments

TL;DR

The paper tackles the challenge of dynamic cloud workloads where static load balancing underperforms. It introduces a Q-learning–based adaptive load balancing framework that continuously learns task distribution policies from real-time server metrics to minimize latency and balance resources. Experimental results in a CloudSim setting show the RL approach reduces response times, improves resource utilization, and increases task completion rates compared with traditional methods, especially under fluctuating workloads. This work demonstrates the potential of AI-driven load management to enhance the efficiency and scalability of modern cloud infrastructures.

Abstract

Efficient load balancing is crucial in cloud computing environments to ensure optimal resource utilization, minimize response times, and prevent server overload. Traditional load balancing algorithms, such as round-robin or least connections, are often static and unable to adapt to the dynamic and fluctuating nature of cloud workloads. In this paper, we propose a novel adaptive load balancing framework using Reinforcement Learning (RL) to address these challenges. The RL-based approach continuously learns and improves the distribution of tasks by observing real-time system performance and making decisions based on traffic patterns and resource availability. Our framework is designed to dynamically reallocate tasks to minimize latency and ensure balanced resource usage across servers. Experimental results show that the proposed RL-based load balancer outperforms traditional algorithms in terms of response time, resource utilization, and adaptability to changing workloads. These findings highlight the potential of AI-driven solutions for enhancing the efficiency and scalability of cloud infrastructures.
Paper Structure (17 sections, 1 equation, 3 figures)

This paper contains 17 sections, 1 equation, 3 figures.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Traditional Load Balancing Techniques
  4. AI-Driven Approaches to Load Balancing
  5. Reinforcement Learning in Load Balancing
  6. Research Gaps and Motivation
  7. Methodology
  8. System Architecture
  9. Reinforcement Learning Model
  10. Experimental Setup
  11. Implementation Details
  12. Experimental Results and Discussion
  13. Response Time
  14. Resource Utilization
  15. Task Completion Rate
  16. ...and 2 more sections

Figures (3)

  • Figure 1: Comparison of average response times under varying workloads for different load balancing algorithms.
  • Figure 2: Comparison of resource utilization for different load balancing algorithms.
  • Figure 3: Comparison of task completion rates for different load balancing algorithms.