Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{198425,
        author = {Dr. N. Chandrakala and Bethi Meghana Reddy and Medha Banda and Bhattu Bharath and Chilla Uday Kiran},
        title = {A Load Balancing Algorithm to Optimize Cloud Computing Applications},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9243-9250},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198425},
        abstract = {Efficient workload management remains a critical challenge in cloud computing environments, particularly within Infrastructure as a Service (IaaS) models where computational resources must be dynamically allocated. Uneven task distribution often results in performance bottlenecks, increased response time, and inefficient resource utilization. This study introduces a priority-driven adaptive load balancing framework designed to enhance system performance by intelligently distributing tasks across virtual machines. The proposed system integrates task scheduling, real-time resource monitoring, and Quality of Service (QoS)-aware allocation strategies. Unlike conventional methods that rely on static or limited dynamic mechanisms, the framework continuously evaluates system conditions, including workload intensity, virtual machine capacity, and task constraints such as deadlines and priority levels. Based on these parameters, tasks are assigned to the most suitable resources to ensure balanced execution. Experimental analysis demonstrates that the proposed approach significantly improves system efficiency by maintaining uniform workload distribution and reducing makes pan. The system achieves resource utilization levels close to 80%, while minimizing idle time and preventing overload scenarios. Additionally, the incorporation of SLA-aware scheduling enhances reliability and user satisfaction. The proposed model provides a scalable and efficient solution for modern cloud environments, addressing key limitations of existing load balancing techniques.},
        keywords = {Adaptive Load Balancing, Cloud Task Scheduling, Virtual Machine Optimization, QoS-Aware Allocation, SLA Management, Dynamic Resource Distribution, IaaS Optimization, Cloud Performance Enhancement.},
        month = {April},
        }