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 across storage devices (i.e., prefetching hot data and evicting cold data) to sustain high HSS performance. These policies are tightly interdependent, and thus, improving one without considering the other leads to suboptimal HSS performance. Unfortunately, prior works focus on optimizing only one of the policies.
  Our goal is to design a holistic data-management technique that optimizes both data-placement and data-migration policies to fully exploit the potential of an HSS. To this end, we propose Harmonia, a multi-agent reinforcement learning (RL)-based data-management technique. Harmonia employs two lightweight autonomous RL agents, a data-placement agent and a data-migration agent, that adapt their policies for the current workload and HSS configuration while coordinating with each other.
  We evaluate Harmonia on real HSS configurations with up to four heterogeneous storage devices and 25 data-intensive workloads. On a performance- (cost-) optimized HSS with two heterogeneous storage devices, Harmonia outperforms the best-performing prior approach by 29.3% (44.8%) on average. On an HSS with three (four) devices, Harmonia outperforms the best-performing prior work by 38.9% (39.2%) on average. Harmonia's performance benefits come with low latency (240 ns for inference) and storage (206 KiB in DRAM for both RL agents combined) overheads.

دراسة جديدة بعنوان 'هارمونية' تقدم نهجًا شاملًا لوضع البيانات وهجرتها في أنظمة التخزين الهجينة (HSS) باستخدام التعلم المعزز متعدد الوكلاء (MARL). تتناول هذه الطريقة الترابط بين سياسات إدارة البيانات لتحسين الأداء في بيئات الحوسبة عالية الأداء.

Un nuevo estudio titulado 'Harmonia' presenta un enfoque holístico para la colocación y migración de datos en sistemas de almacenamiento híbridos (HSS) utilizando aprendizaje por refuerzo multiagente (MARL). Este método aborda la interdependencia de las políticas de gestión de datos para optimizar el rendimiento en entornos de computación de alto rendimiento.

Une nouvelle étude intitulée 'Harmonia' présente une approche holistique pour le placement et la migration des données dans les systèmes de stockage hybrides (HSS) en utilisant l'apprentissage par renforcement multi-agents (MARL). Cette méthode aborde l'interdépendance des politiques de gestion des données pour optimiser les performances dans les environnements de calcul haute performance.

A new study titled 'Harmonia' introduces a holistic approach to data placement and migration in hybrid storage systems (HSS) using multi-agent reinforcement learning (MARL). This method addresses the interdependence of data management policies to optimize performance in high-performance computing environments.