FleetIO: Managing Multi-Tenant Cloud Storage with Multi-Agent Reinforcement Learning

Cloud platforms have been virtualizing storage devices like flash-based solid-state drives (SSDs) to make effective use of storage resources. They enable either software-isolated instance or hardware-isolated instance for facilitating the storage sharing between multi-tenant applications. However, for decades, they have to combat the fundamental tussle between the performance isolation and resource utilization. They suffer from either long tail latency caused by weak isolation or low storage utilization caused by strong isolation.

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Publication Details

Published
2025-02-06
DOI
https://doi.org/10.1145/3669940.3707229
Citations
4
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
12.98
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article

FleetIO: Managing Multi-Tenant Cloud Storage with Multi-Agent Reinforcement Learning

Jian Huang, Jichuan Chang, Daixuan Li, Marc Snir et al.
4 citations
Cloud Computing and Resource Management
12.98
article

FleetIO: Managing Multi-Tenant Cloud Storage with Multi-Agent Reinforcement Learning

Jian Huang, Jichuan Chang, Daixuan Li, Marc Snir, Jinghan Sun, Benjamin Reidys
article en
4 citations

Abstract

Cloud platforms have been virtualizing storage devices like flash-based solid-state drives (SSDs) to make effective use of storage resources. They enable either software-isolated instance or hardware-isolated instance for facilitating the storage sharing between multi-tenant applications. However, for decades, they have to combat the fundamental tussle between the performance isolation and resource utilization. They suffer from either long tail latency caused by weak isolation or low storage utilization caused by strong isolation.

Google (United States) (US), Urbana University (US)
Openalex Percentile: Top 2%
Cloud Computing and Resource Management
12.98
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