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Ravi Kumar Vankayalapati

Majjari Venkata Kesava Kumar

Abstract

Real-time analytics has long held the promise of understanding customers, systems, and computing on the fly, unlocking new insights and data-driven decision-making. However, advancements in data processing, specifically faster, cheaper, and smarter storage for big data, have yet to be realized to make real-time analytical systems a reality. This paper shows how recent advancements in storage technologies have begun to change the game with three critical developments. The first is the development of byte-addressable persistent memory that serves as high-speed storage. The second technology is direct attached storage that minimizes the CPU stack load when accessing data on the device, keeping more of the computer in the data store. Lastly, stateless computational storage targets distributed AI systems, pushing AI computations closer to the data source. These high-speed storage innovations have already begun to be integrated into cloud frameworks, enabling storage stacks to manage these devices for users and creating a roadmap for integrating stateless computing storage as that sector matures. The paper outlines these AI-centric storage designs, explains the coming transformations to cloud computing that these storage innovations are enabling, details their status, and describes the kinds of applications that will improve with these storage designs. These changes are released from two perspectives, one for AI and data processing in the cloud, sustaining data analytics, and another for stream processing in time-series systems. Of particular importance in these designs are the storage controllers, which will be an integral part of AI-optimized data storage critical for real-time and edge settings, showing the latest storage that aims to push computation toward data and keep more of the bits intact as data are transformed at the edge with minimally powerful AI accelerators.

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