Harmonia: Enhancing Data Placement and Migration in Hybrid Storage Systems via Multi-Agent Reinforcement Learning
arXiv:2503.20507v4 Announce Type: replace-cross Abstract: Modern high-performance computing (HPC) environments rely on hybrid storage systems (HSS) that combine multiple storage devices with diverse latency, bandwidth, endurance, and capacity characteristics to meet the performance, capacity, and cost requirements of data-intensive applications. The performance of an HSS highly depends on two key data-management policies: (1) data placement, which determines the most suitable storage device to store application data, and (2) data migration, which dynamically reorganizes previously-stored data
The increasing complexity and data intensity of high-performance computing (HPC) environments necessitate more sophisticated and autonomous data management solutions to optimize performance and cost.
This development indicates a growing capability to automate and optimize the foundational infrastructure for AI and large-scale data processing, which will be crucial for scaling advanced AI applications and reducing operational overhead.
Data placement and migration in hybrid storage systems can now be dynamically optimized by multi-agent reinforcement learning, moving beyond static policies to adaptive, AI-driven management.
- · HPC providers
- · Cloud infrastructure companies
- · AI/ML application developers
- · Data center operators
- · Organizations with legacy data management systems
- · Human IT administrators focused on manual storage optimization
HPC environments will experience improved performance, efficiency, and reduced operational costs due to intelligent data management.
The enhanced efficiency of underlying compute infrastructure could accelerate the development and deployment of more complex AI models.
As data infrastructure becomes more autonomous and self-optimizing, it could shift priorities in compute supply chains towards components that best support such intelligent systems rather than raw, unoptimized capacity.
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Read at arXiv cs.LG