A COMPARATIVE STUDY OF TRADITIONAL, DYNAMIC AND ELASTIC LOAD BALANCING ALGORITHMS IN CLOUD COMPUTING

Elastic Load Balancing (ELB) in cloud computing is essential for managing dynamic workloads by distributing traffic across multiple computing resources to ensure high availability, scalability, and performance.Various ELB algorithms have been developed to meet the evolving demands of cloud environments.Traditional methods such as Round Robin and Least Connection offer basic traffic distribution, while more advanced strategies like resource-based, weighted, and latencyaware algorithms provide intelligent routing based on server health, capacity, and network conditions.Recent advancements incorporate AI and machine learning to enable predictive and adaptive load balancing, allowing systems to automatically respond to traffic fluctuations and optimize resource usage in real-time.Additionally, geolocation-based routing enhances user experience by directing requests to the nearest or fastest nodes, particularly valuable in edge computing and global service delivery.These algorithms are often integrated with infrastructure-as-code tools and DevOps workflows for enhanced automation and observability.As cloud applications become more complex and distributed, ELB algorithms continue to evolve, focusing on greater intelligence, flexibility, and cross-platform compatibility to support real-time applications, IoT systems, and serverless architectures.Unlike traditional load balancers that rely on hardware and static rules, ELB dynamically adjusts its capacity based on real-time traffic changes.In this paper a study on cloud load balancing has been discussed along with comparative study with dynamic and elastic load balancing.Future prospect of ELB has also been included in this research article.