Optimizing AI Cluster Performance with High-Speed Storage Solutions in Cloud-Integrated Environments

Abstract

This article analyzes optimized storage solutions focusing on high-speed, robust random-access storage to alleviate performance challenges faced by Artificial Intelligence (AI) clusters in cloud-integrated environments. Advanced storage subsystem performance is integrated. Significant research for cloud environments, particularly in the application of advanced storage hardware or software devices to improve AI cluster performance, isn’t detected. Standard disk drives’ storage performance is not satisfactory for AI clusters to produce, train models, and perform model inference. Their random read-write I/O is poor, better for sequential access only. Thus, generic storage is not optimized for the real-time probing and querying of the AI models cluster. This shortcoming affects the service quality of the AI applications. In point of fact, the exclusive I/O usage and patterns of AI applications form a demand for improved storage solutions. For instance, for training iterative models, data should be loaded from storage rapidly to GPUs, trained, and saved, which needs a low-latency, random-read, high-concurrent storage environment. The model's parameters repeatedly change in model inference, so storage should support the real-time writing and modification of massive, small data files while fast reading large model files. For boosting the performance of batch inference tasks, the query index of the AI model should be utilized for ballooning, requiring storage to read models at a high speed to build memory. Apart from these, the crucial factor to increase the efficiency of the AI cluster is the efficient sharing, processing, and data flow of the general and AI data, forming patterns like streaming and caching. Ease of use and generic storage doesn’t fulfill the above requirements.