This research paper conducts a thorough and comparative analysis of various load balancing algorithms in cloud computing environments, aiming to provide valuable insights for cloud administrators and architects in their decision-making process. The research methodology involves creating a controlled experimental environment that mirrors typical cloud infrastructures. Performance metrics such as demand response time, server load, throughput, demand distribution fairness, scalability, and resource usage are considered, with real data from cloud environments enhancing the analysis. Both quantitative and qualitative data, supported by advanced statistical tools and data visualization techniques, are employed to shed light on the overall merits of each load balancing algorithm. This research empowers cloud professionals with the knowledge to navigate intricate realm of load balancing in cloud computing, thereby enhancing the performance, flexibility, and scalability of their cloud systems.
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Mowade et al. (2024) studied this question.
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