An Improved Performance Analysis for AWS Cloud Load Balancing Using Weighted Round Robin (WRR) Technique

A critical component of cloud computing, load balancing ensures that incoming network traffic is shared across multiple servers in a manner that optimizes scalability, availability, and performance. The aim of this work is to improve the management of cloud-based applications by leveraging Amazon Web Services (AWS) to create an efficient load balancing solution. In order to prevent server overload and to maintain high availability and fault tolerance, the suggested method evenly divides client requests among several servers. This paper will use AWS Elastic Load Balancing (ELB) to automatically divide application traffic among several Amazon EC2 instances. The ELB service will adapt to traffic in real time, offering a reliable and scalable way to handle erratic workloads. To find the best approach for particular use scenarios, several load balancing algorithms—including round-robin, least connections, and IP hash—will be investigated. The paper also integrates auto-scaling to automatically adjust the number of running instances based on real-time traffic demands, reducing costs by scaling down during low traffic periods. By utilizing AWS Cloud Watch, the system will monitor application performance and traffic patterns, enabling proactive management and timely responses to potential issues. This paper demonstrates the effective use of Java tools to build a cloud infrastructure that is resilient, scalable, and capable of maintaining consistent application performance, even under high demand Some of its characteristics include task type, task priority, task status, execution time, energy efficiency, CPU and memory usage, network traffic, power consumption, and the quantity of executed instructions. The implementation will serve as a blueprint for businesses seeking to optimize their cloud-based applications, ensuring a seamless user experience through effective load balancing strategies.