Reinforcement Learning and Genetic Algorithm-Based Approach for Load Balancing and Resource Optimization in Cloud Data Centers

Author(s)	Swapnil R. Kadam, Devaseelan S., Amolkumar N. Jadhav
Country	India
Abstract	This review explores the integration of Reinforcement Learning (RL) and Genetic Algorithms (GA) for load balancing and resource optimization in cloud data centers. The paper examines state-of-the-art approaches, their advantages, challenges, and potential hybrid methodologies combining RL's decision-making capabilities with GA's search optimization strengths. The survey aims to highlight how these techniques improve performance metrics like resource utilization, energy efficiency, and system reliability while addressing scalability and dynamic workload challenges.
Keywords	Reinforcement Learning (RL), Genetic Algorithms (GA), Load Balancing, Scalability, Energy Efficiency, Cloud Data Centers, Resource Optimization, System Reliability
Field	Computer
Published In	Volume 16, Issue 1, January-March 2025
Published On	2025-02-02
Cite This	Reinforcement Learning and Genetic Algorithm-Based Approach for Load Balancing and Resource Optimization in Cloud Data Centers - Swapnil R. Kadam, Devaseelan S., Amolkumar N. Jadhav - IJSAT Volume 16, Issue 1, January-March 2025.

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