AI has been widely applied in fields such as image recognition, natural language processing, autonomous driving, and medical impact analysis. The scale of AI data is huge, and its data density, concurrent access frequency, and unstructured proportion all far exceed the scope of traditional computing. More stringent requirements are placed on the storage systems of data centers. Traditional storage architectures cannot meet the demands of high throughput, low latency, and large-scale parallel data reading and writing for AI training and inference, nor can they strike a balance among performance, efficiency, and cost.
The first step in optimizing AI data storage management is to decouple the storage architecture. AI training data often exists at the TB or even PB level, and most of them are unstructured files such as images, audio, and video. The access method is mainly sequential read and write, and the traditional block storage method is difficult to meet this demand. Data centers are beginning to shift towards a combination of object storage and distributed file systems, supporting horizontal scaling, multi-replica redundancy, and concurrent access across nodes. The metadata mechanism of object storage can quickly handle the retrieval requirements of massive files, while the distributed file system can take into account both high concurrent access and the collaborative processing of high-speed cache. The combination of the two can significantly improve the efficiency of AI data loading and reduce the I/O bottleneck problem in the training process.
Secondly, data centers have introduced higher-performance Storage media at the hardware level, such as NVMe SSD and SCM (Storage Class Memory). Traditional HDDS cannot meet the demands of AI for low latency and high bandwidth. Therefore, high-performance flash memory has become the main carrier for AI training data. The NVMe protocol has shorter command queue paths, lower latency and higher I/O performance. Combined with the RDMA network to build an end-to-end high-speed path, it can significantly shorten the data access latency during the model training process and improve the overall training efficiency. While SCM lies between DRAM and SSD, it can act as a cache layer in some frequently invoked data paths to accelerate the loading of hot data during the training process.
At the software scheduling level, modern data centers have extensively introduced data-aware management platforms to uniformly manage data throughout the entire life cycle of AI. From collection, preprocessing, training, inference to archiving, each stage of AI data requires the storage system to have different strategy support.
The data center still needs to solve the problems of sharing and isolating AI data. In large-scale AI training tasks, it is often the case that multiple nodes access the same dataset simultaneously, posing a challenge to the concurrent processing capability of the storage system. By constructing a distributed concurrent file access mechanism, supporting concurrent reading and writing across multiple nodes, and in combination with the data consistency protocol and cache consistency synchronization mechanism, it ensures that the data states obtained by different computing nodes remain consistent.
In terms of ensuring data security, modern data centers typically adopt end-to-end encryption technology, in conjunction with key management systems, to guarantee the confidentiality of data throughout the entire process of storage, transmission, and access. Meanwhile, the behavior log system is used to monitor the data access behavior in real time, issue alerts and track abnormal operations, providing technical support for the controllable use of AI data.
In response to the temporary storage requirements for massive intermediate result data during the AI training process, the data center has also optimized the caching and relay mechanisms. By introducing technical means such as GPU local cache, collaborative cache of training nodes, and edge cache nodes, performance bottlenecks caused by frequent access to remote storage can be avoided. Some platforms also deploy AI-aware storage schedulers, which can dynamically adjust the data caching strategy based on the model iteration frequency and data usage popularity, thereby reducing unnecessary data transmission and improving the overall computing throughput.
Finally, the rapid growth of AI data also poses challenges to the energy efficiency and cost of data centers. In terms of energy conservation and sustainable development, when designing AI storage systems, data centers will take into account cold and hot data migration, resource redistribution mechanisms, perception and replacement strategies for storage hardware aging, etc., to achieve dynamic resource release and on-demand allocation.
In summary, the data center is undergoing in-depth optimization from multiple aspects such as architecture design, medium selection, software scheduling, data lifecycle management, sharing isolation, security control, caching mechanism, system scalability, and energy cost control, to build a new generation of data storage management system for AI applications.
