Secure Load Balancing for Big Data Analytics in Green Cloud Computing Environments

Load balancing has been one of the hard decision-making factors in modern-day big data analytics and cloud computing ecosystem. Due to the expanding scale and variety of data, striking an effective balance in load balancing for large-scale data processing is becoming more urgent, particularly in a real-time manner in cloud environments. Furthermore, the explosive development of cloud computing has created a pressing demand for green energy and energy-efficient solutions in data centers to reduce carbon emissions. The main contribution of this paper is a secure load-balancing scheme for big data analytics in green cloud computing environments, where challenges such as energy-efficient resource usage with computation efficiency and privacy preservation are considered. This approach is based on a data partitioning operation for workload distributed among several virtual machines, considering energy consumption and resource availability. The framework also has a secure data exchange protocol to retrieve sensitive information during load balancing. This is demonstrated by simulation results, which reveal that the proposed method can acquire load balance with data security and power consumption efficiency. The results indicate that our solution performs better in data privacy, energy efficiency, and resource utilization than previously implemented load-balancing methods.