Optimization of network performance of distributed storage system for biomechanical big data based on cloud computing
Abstract
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.
References
1. Newcomb ME, Swann G, Addington EL, et al. Randomized controlled trial of a relationship education and HIV prevention program for young male couples: Biomedical and behavioral outcomes. Health Psychology. 2025; 44(3): 297-309. doi: 10.1037/hea0001448
2. Li G, Zhang Y, Shi Y, et al. Distributed Coordinated Control Strategy for Grid-Forming-Type Hybrid Energy Storage Systems. Sustainability. 2025; 17(4): 1436. doi: 10.3390/su17041436
3. Ning Y, Lin H, Wan X, et al. Study on Instability Mechanism and Compensation Strategy for Distributed Energy Storage Systems. Electronics. 2024; 13(23): 4808. doi: 10.3390/electronics13234808
4. Rigo-Mariani R, Debusschere V. A two-stage coordination strategy for the control of distributed storage at the household level—Arbitrage between users preferences and distribution grid objectives. Mathematics and Computers in Simulation. 2024; 224: 111-127. doi: 10.1016/j.matcom.2023.08.033
5. Kim C, Chon KW. Accelerating erasure coding by exploiting multiple repair paths in distributed storage systems. Cluster Computing. 2024; 27(6): 8621-8635. doi: 10.1007/s10586-024-04438-y
6. Majidi M, Parvania M, Byrne R. Risk‐based stochastic scheduling of centralised and distributed energy storage systems. IET Smart Grid. 2023; 6(6): 596-608. doi: 10.1049/stg2.12125
7. Yin C, Xu Z, Li W, et al. Erasure Codes for Cold Data in Distributed Storage Systems. Applied Sciences. 2023; 13(4): 2170. doi: 10.3390/app13042170
8. Wen X, Hao S, Liu S, et al. Microstructure and corrosion behavior of Ti–10Mo–6Zr–4Sn–3 Nb (Ti-B12) alloys as biomedical material in lactic acid-containing Hank’s solution. International Journal of Electrochemical Science. 2025; 20(4): 100974. doi: 10.1016/j.ijoes.2025.100974
9. Hamandawana P, Cho DJ, Chung TS. Speed-Dedup: A New Deduplication Framework for Enhanced Performance and Reduced Overhead in Scale-Out Storage. Electronics. 2024; 13(22): 4393. doi: 10.3390/electronics13224393
10. Fan Y, Li Z, Huang X, et al. An Energy Management System for Distributed Energy Storage System Considering Time-Varying Linear Resistance. Electronics. 2024; 13(21): 4327. doi: 10.3390/electronics13214327
11. Kim C, Chon KW. Correction: Accelerating erasure coding by exploiting multiple repair paths in distributed storage systems. Cluster Computing. 2024; 27(6): 8637-8637. doi: 10.1007/s10586-024-04574-5
12. Chang X, Li R, Wang Y, et al. A Two-Stage SOC Balancing Control Strategy for Distributed Energy Storage Systems in DC Microgrids Based on Improved Droop Control. Journal of Electrical Engineering & Technology. 2024; 19(7): 3891-3905. doi: 10.1007/s42835-024-01835-6
13. Zheng GY, Zeng T, Li XY. Application and prospect of cutting-edge information technology in biomedical big data. Yi Chuan. 2021; 43(10): 924-929.
14. Manduchi E, Fu W, Romano JD, et al. Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses. BMC Bioinformatics. 2020; 21(1). doi: 10.1186/s12859-020-03755-4
15. Richter AN, Khoshgoftaar TM. Sample size determination for biomedical big data with limited labels. Network Modeling Analysis in Health Informatics and Bioinformatics. 2020; 9(1). doi: 10.1007/s13721-020-0218-0
16. Nunome A, Hirata H. Adaptive Parameter Tuning for Constructing Storage Tiers in an Autonomous Distributed Storage System. International Journal of Networked and Distributed Computing. 2022; 10(1-2): 1-10. doi: 10.1007/s44227-022-00004-3
17. Zhou B, Lu L. An effective 3-D fast fourier transform framework for multi-GPU accelerated distributed-memory systems. The Journal of Supercomputing. 2022; 78(15): 17055-17073. doi: 10.1007/s11227-022-04491-7
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