Offloading Artificial Intelligence Workloads across the Computing Continuum by means of Active Storage Systems

TL;DR

This paper introduces a software architecture that leverages active storage systems within the computing continuum to optimize AI workload distribution, reducing data transfer, improving resource utilization, and accelerating training processes.

Contribution

It presents a novel architecture integrating active storage with AI workloads across heterogeneous devices, demonstrated through implementation with Python and dataClay, enhancing scalability and efficiency.

Findings

Significant reduction in memory consumption and data transfer overhead.

Improved training times across diverse devices.

Maintained accuracy while enhancing performance.

Abstract

The increasing demand for artificial intelligence (AI) workloads across diverse computing environments has driven the need for more efficient data management strategies. Traditional cloud-based architectures struggle to handle the sheer volume and velocity of AI-driven data, leading to inefficiencies in storage, computation, and data movement. This paper explores the integration of active storage systems within the computing continuum to optimize AI workload distribution. By embedding computation directly into storage architectures, active storage is able to reduce data transfer overhead, enhancing performance and improving resource utilization. Other existing frameworks and architectures offer mechanisms to distribute certain AI processes across distributed environments; however, they lack the flexibility and adaptability that the continuum requires, both regarding the heterogeneity of devices and the rapid-changing algorithms and models being used by domain experts and researchers. This article proposes a software architecture aimed at seamlessly distributing AI workloads across the computing continuum, and presents its implementation using mainstream Python libraries and dataClay, an active storage platform. The evaluation shows the benefits and trade-offs regarding memory consumption, storage requirements, training times, and execution efficiency across different devices. Experimental results demonstrate that the process of offloading workloads through active storage significantly improves memory efficiency and training speeds while maintaining accuracy. Our findings highlight the potential of active storage to revolutionize AI workload management, making distributed AI deployments more scalable and resource-efficient with a very low entry barrier for domain experts and application developers.