Simulation Design of Load Balancing Optimization for Cloud Computing Data Stream Storage Based on Big Data Algorithms

Optimizing the storage load balancing of data streams in cloud computing can improve the uniformity of data stream storage distribution within the network. The traditional method’s processing power is only limited to the window range that a certain operator can handle at the node. When the data gradually increases, the processing power is insufficient, which can easily lead to data flow congestion. Moreover, it ignores the research on the load distribution of the entire system and migration strategies in dynamic load balancing. Optimizing load balancing of data streams requires evaluating the load situation of data stream storage nodes, transferring high-intensity loads to nodes with lighter loads, and completing the optimization of data stream storage load balancing. To address the shortcomings of existing load balancing methods, this paper proposes a simulation design method for optimizing load balancing of cloud computing data stream storage based on big data algorithms. This method analyzes the historical load data of storage nodes, uses big data algorithms to predict the future load situation of nodes, and dynamically allocates data to corresponding nodes based on this to achieve balanced distribution and optimization of load. The experimental results show that the proposed method has high feasibility, good computational performance, and dynamic load balancing.