ISVABI: In-Storage Video Analytics Engine with Block Interface

Yi Zheng, Joshua Fixelle, Pingyi Huo, Mircea Stan, Michael Mesnier, Vijaykrishnan Narayanan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The wide use of cameras in the past decade has increased the need to process video data significantly. Due to the large volume of video data, analyzing videos to extract useful information has become a critical challenge. Several prior works have tried to accelerate video analytics workloads by offloading some operations to embedded processors within storage devices. However, most of these works require changing the block I/O interface of a normal solid-state drive (SSD) to a key-value interface, which in turn changes data structures within SSDs and requires re-programming existing applications when deploying these designs to existing warehouse-level data centers. In addition, when processing large videos, key-value SSDs perform two times slower than block I/O interface SSDs. In this work, we propose ISVABI, a software/firmware approach that maintains the SSD block I/O interface which provides offloaded operations for user-space video analytics workloads without requiring SSD hardware modification. We implement ISVABI on the Cosmos+ OpenSSD platform and show that the proposed ISVABI outperforms normal SSDs by 4.18x for various types of video operations while consuming 16% less power. We evaluate ISVABI on five real-world video analytics workloads and show a 1.89x end-to-end latency improvement.

Original languageEnglish (US)
Title of host publicationLCTES 2023 - Proceedings of the 24th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems
EditorsBernhard Egger, Dongyoon Lee
PublisherAssociation for Computing Machinery
Pages111-121
Number of pages11
ISBN (Electronic)9798400701740
DOIs
StatePublished - Jun 13 2023
Event24th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems, LCTES 2023 - Orlando, United States
Duration: Jun 18 2023 → …

Publication series

NameProceedings of the ACM SIGPLAN Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES)

Conference

Conference24th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems, LCTES 2023
Country/TerritoryUnited States
CityOrlando
Period6/18/23 → …

All Science Journal Classification (ASJC) codes

  • Software

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