Analyzing Data Access Characteristics of AIoT Workloads for Efficient Write Buffer Management

Abstract

As deep learning technologies increasingly influence various aspects of human life, the emerging paradigm of Artificial Intelligence of Things (AIoT) is gaining significant attention. AIoT, which relies on large datasets and complex models, poses significant challenges to the limited resources of mobile systems. Many solutions have been proposed to address these challenges, including computation offloading to edge or cloud servers. In this article, we demonstrate that analyzing data access patterns and managing them efficiently are also crucial for further enhancing the performance of AIoT systems. Specifically, we propose an efficient write buffer management scheme tailored to mobile AIoT workloads. Our approach is based on an extensive analysis of data access patterns, revealing that the conventional buffer cache architectures and algorithms are inefficient for AIoT workloads due to access characteristics such as write dominance and long repetitive loops. To address these issues, we adopt a nonvolatile write buffer at the storage layer and judiciously manage write loops. The key contributions of our study are as follows: 1) despite relying solely on limited flush information at the storage layer, our scheme achieves performance comparable to full loop detection at the host system; 2) our scheme requires no modifications to host OS semantics or buffer cache interfaces, ensuring high adaptability; and 3) experimental results demonstrate that our scheme significantly reduces storage traffic, outperforms representative algorithms such as LRU, LFU, LIRS, CLOCK-Pro, and LeCaR, and enhances data access efficiency for AIoT workloads.