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@article{167092,
        author = {MAHENDRA  and Deepshikha Josh},
        title = {Optimized Load Balancing Techniques for Cloud Computing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {3},
        pages = {338-343},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167092},
        abstract = {Cloud registration facilitates the flow of information and provides users with valuable resources, billing customers only for resource usage. Cloud computing saves data and ensures accessibility, with increased transparency leading to higher information hoarding tendencies. Stack adjustment tests are specifically designed for cloudy conditions. Load adjustment evenly distributes dynamic workloads across hubs to avoid overloading, legalize assets, and enhance system performance. Most current algorithms enable stack modification and improved asset utilization, using memory, CPU, and system stacks. The load adjustment system identifies overloaded hubs and redistributes the load to less burdened hubs. Load balancing ensures workloads are evenly distributed across cloud data centers, preventing any from being overwhelmed or underutilized. This study proposes a hybrid approach combining Honey Bee (HB) with Particle Swarm Optimisation (PSO) for an optimal load balancing strategy, evaluated using the CloudSim simulator. The hybrid method outperforms the individual approaches of HB and PSO in terms of response time, request processing, data center utilization, and virtual machine costs, demonstrating enhanced system responsiveness and efficiency.},
        keywords = {Cloud Registration , Load Balancing , Hybrid Approach, Honey Bee (HB) Algorithm , Particle Swarm Optimization (PSO).},
        month = {August},
        }