Containerized In-Storage Processing and Computing-Enabled SSD Disaggregation
Miryeong Kwon, Donghyun Gouk, Eunjee Na, Jiseon Kim, Junhee Kim, Hyein Woo, Eojin Ryu, Hyunkyu Choi, Jinwoo Baek, Hanyeoreum Bae, Mahmut Kandemir, Myoungsoo Jung

TL;DR
DockerSSD introduces a containerized in-storage processing framework on SSDs, utilizing virtualization and lightweight firmware to improve performance and scalability for data analytics and large-scale AI inference.
Contribution
It presents DockerSSD, a novel ISP model combining OS virtualization and firmware techniques for efficient, disaggregated SSD-based data processing.
Findings
Up to 2.0x performance improvement for I/O workloads
7.9x faster distributed LLM inference
Supports large-scale, disaggregated storage pools
Abstract
ISP minimizes data transfer for analytics but faces challenges in adaptation and disaggregation. We propose DockerSSD, an ISP model leveraging OS-level virtualization and lightweight firmware to enable containerized data processing directly on SSDs. Key features include Ethernet over NVMe for network-based ISP management and Virtual Firmware for secure, efficient container execution. DockerSSD supports disaggregated storage pools, reducing host overhead and enhancing large-scale services like LLM inference. It achieves up to 2.0x better performance for I/O-intensive workloads, and 7.9x improvement in distributed LLM inference.
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Taxonomy
TopicsAdvanced Data Storage Technologies · Cloud Computing and Resource Management · Cloud Data Security Solutions
