Distributed storage was first proposed by Google. Its purpose is to use cheap servers to provide web access problems in large-scale and high-concurrency scenarios. It adopts an expandable system structure, uses multiple storage servers to share storage load, and uses location servers to locate and store information. It not only improves the reliability, availability and access efficiency of the system, but is also easy to expand.
Compared with traditional centralized server storage methods, distributed storage has significant advantages in storage efficiency, bandwidth utilization, security, privacy, access speed, etc., especially in high-performance computing, big data video It has a wide range of applications in application scenarios such as cloud and big data analysis.
High Performance Computing Scenarios
Cluster-based high-performance computing has become a necessary auxiliary tool in fields such as meteorology and climate, geological exploration, aerospace, engineering computing, and material engineering. The cluster system has strong scalability. By adding or deleting nodes in the cluster, the processing capacity of the system can be increased or decreased at any time without affecting the original application and computing tasks. According to different computing modes and scales, the number of nodes constituting the cluster system can range from a few to tens of thousands. These services put forward new requirements for the back-end storage system, including unified storage space, high-efficiency file retrieval, high-bandwidth throughput performance, and highly reliable data security guarantee.
Big Data Video Cloud Application Scenarios
With the popularization of high-definition video technology and ultra-high-definition technology, video big data application scenarios, such as Xueliang Project, Safe City, radio and television media assets, film and television production, video websites and other fields, have proposed large capacity, high read and write performance, and Requirements for high reliability, low latency, and scalability. For such large-scale video data application scenarios, a storage system with advanced technology and superior performance is required as the supporter for back-end data storage.
Application Scenarios of Big Data Analysis
With the development of Internet technology and artificial intelligence, various big data analysis and artificial intelligence business models based on massive users/data/terminals continue to emerge. It is also necessary to fully consider the integration of storage functions, data security, and data stability. Scalability, performance and cost factors.
In the "digital age" of explosive data growth, software-defined distributed storage is the crystallization of the rapid development of storage technology, and has a lot of room for growth, and will definitely be applied to a wider range of big data business scenarios.
As a powerful distributed dynamic cloud storage service platform, Filecoin is a representative of blockchain technology 3.0 in the era of big data storage, making it the focus of global attention. With the continuous improvement of Filecoin's ecological construction and the continuous manifestation of value, it will definitely be recognized and participated by more investors and investment institutions in the future, and the appreciation prospect is broad.