Dynamic Load Balancing for Improved Resource Allocation in Cloud Environments

Abstract

Cloud computing has emerged as a revolutionary paradigm in information technology, offering scalable and on-demand access to computing resources. Efficient resource allocation is a crucial aspect in ensuring optimal performance and cost-effectiveness of cloud environments. Load balancing algorithms play a vital role in distributing workloads across available resources, preventing resource overutilization and underutilization. This research focuses on optimizing resource allocation in cloud computing through the application of advanced load balancing algorithms. The primary objective is to enhance resource utilization, minimize response time, and improve overall system performance. Traditional load balancing techniques often fall short in addressing the dynamic and heterogeneous nature of cloud environments. Therefore, this study investigates novel algorithms that leverage real-time resource monitoring, predictive analytics, and adaptive decision-making to intelligently allocate workloads. The research involves the design, implementation, and evaluation of multiple load balancing algorithms. Comparative analysis is conducted to assess the efficiency of the proposed algorithms against existing methods. Performance metrics such as response time, throughput, resource utilization, and scalability are used to gauge the effectiveness of the algorithms under various workload scenarios. this research explores the impact of load balancing strategies on energy consumption and environmental sustainability. Energy-efficient load balancing algorithms are developed to align resource allocation with energy consumption patterns, contributing to the overall green computing initiative.