Research on Performance Optimization of Massive Open Online Course Platform Using a Hash-Based Load Balancing Algorithm

This paper proposes a hash load balancing algorithm based on cloud computing environment, aiming to improve the resource allocation efficiency and system stability of MOOe platform. In the design of resource allocation strategy, a multidimensional hash model combining course access volume and resource demand is constructed. By dynamically analyzing user access behavior and resource usage pattern, a solution is provided for hot issues that are difficult to solve with traditional methods. The model can flexibly allocate resources for different courses to avoid excessive concentration or vacancy of resources, thereby optimizing the overall performance of the platform. To verify the actual effect of the algorithm, this paper conducts a simulation experiment of multi-user concurrent access scenario, focusing on load balancing performance and system throughput. The hash-based algorithm shows significant advantages, which improves resource utilization by 18.4% and shortens the response time by 23.6% on average. In addition, the algorithm effectively reduces the system crash rate caused by uneven load distribution, further improving the stability of the platform. The load balancing algorithm performs well in the large-scale user access scenario of MOOC platform, which not only significantly improves resource utilization efficiency and user experience, but also enhances the reliability and elasticity of the system.