Optimization of network performance of distributed storage system for biomechanical big data based on cloud computing

This study proposes a network performance optimization strategy based on cloud computing to address the stringent demands of biomechanical big data on the efficiency of distributed storage systems. Biomechanical data, including motion capture, force plate measurements, and tissue strain analysis, involve large-scale, high-frequency, and heterogeneous datasets that necessitate efficient storage and real-time processing. By optimizing data transmission paths, designing an efficient caching mechanism, dynamically allocating bandwidth resources, and implementing network congestion control, the system significantly enhances throughput, reduces latency, and improves bandwidth utilization and data transmission reliability.