A Dynamic Resource-Aware Load Balancing Approach for Optimized Performance in Cloud Computing

doi:10.23940/ijpe.26.02.p6.110118

Abstract

Abstract:

Cloud computing has emerged as a dominant paradigm in response to the increased need for effective computing services over the internet, data sharing, and resource utilization. Efficient load balancing is crucial in managing resources efficiently for improving the performance, scalability, and reliability of cloud computing environments. This paper presents a comparative performance analysis of three dynamic load balancing algorithms — Least Loaded, Weighted Round Robin, and Enhanced Load Balancing (ELB) — for optimizing ERP component allocation in cloud environments. Experiments were conducted using a python-based simulation that dynamically allocates Enterprise Resource Planning (ERP) components to virtualized cloud resources under both uniform and non-uniform configurations. Simulation results reveal that under uniform configuration, ELB achieved the highest CPU utilization (79%) and throughput (295 units) with the lowest response time (39.31%) as compared to traditional algorithms. Similarly, under non-uniform configuration, ELB maintained superior performance with maximum CPU utilization of 78.89%, average utilization of 61.43%, and throughput of 281 units. These results highlight ELB’s capability to adapt dynamically to workload variations while efficiently utilizing computational resources and reducing latency. The ELB approach enhances CPU utilization and overall system responsiveness, as a feasible solution for scalable and adaptive ERP-based cloud environments.

Key words: cloud computing, load balancing, ERP components, resource utilization, throughput, response time, uniform configuration, non-uniform configuration, least loaded, weighted round robin, enhanced load balancing

Sunaina Mehta, and Sushil Bhardwaj. A Dynamic Resource-Aware Load Balancing Approach for Optimized Performance in Cloud Computing [J]. Int J Performability Eng, 2026, 22(2): 110-118.

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