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Efficient Hierarchical Storage Management Empowered by Reinforcement Learning
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. (ISCL)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. (ISCL)ORCID iD: 0000-0001-7273-7923
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. (ISCL)ORCID iD: 0000-0003-0302-6276
2023 (English)In: IEEE Transactions on Knowledge and Data Engineering, ISSN 1041-4347, E-ISSN 1558-2191, Vol. 35, p. 5780-5793Article in journal (Refereed) Published
Abstract [en]

With the rapid development of big data and cloud computing, data management has become increasingly challenging. Over the years, a number of frameworks for data management have become available. Most of them are highly efficient, but ultimately create data silos. It becomes difficult to move and work coherently with data as new requirements emerge. A possible solution is to use an intelligent hierarchical (multi-tier) storage system (HSS). A HSS is a meta solution that consists of different storage frameworks organized as a jointly constructed storage pool. A built-in data migration policy that determines the optimal placement of the datasets in the hierarchy is essential. Placement decisions is a non-trivial task since it should be made according to the characteristics of the dataset, the tier status in a hierarchy, and access patterns. This paper presents an open-source hierarchical storage framework with a dynamic migration policy based on reinforcement learning (RL). We present a mathematical model, a software architecture, and implementations based on both simulations and a live cloud-based environment. We compare the proposed RL-based strategy to a baseline of three rule-based policies, showing that the RL-based policy achieves significantly higher efficiency and optimal data distribution in different scenarios.

Place, publisher, year, edition, pages
IEEE, 2023. Vol. 35, p. 5780-5793
Keywords [en]
Data Management, Cloud Computing, Hierarchical Storage System, Data Migration, Reinforcement Learning
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:uu:diva-490399DOI: 10.1109/tkde.2022.3176753ISI: 000981944600024OAI: oai:DiVA.org:uu-490399DiVA, id: diva2:1717791
Projects
eSSENCE - An eScience Collaboration
Funder
Swedish Foundation for Strategic Research, BD15-0008Available from: 2022-12-09 Created: 2022-12-09 Last updated: 2024-12-16Bibliographically approved
In thesis
1. Intelligent Data Management via Machine Learning: From Storage Hierarchy to Information Hierarchy
Open this publication in new window or tab >>Intelligent Data Management via Machine Learning: From Storage Hierarchy to Information Hierarchy
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The rise of Big Data has catalyzed numerous advanced data-driven methods, while simultaneously posing significant challenges in data management. This thesis aims to address two fundamental aspects of data management–storage management and information extraction–by leveraging machine learning (ML) techniques. In particular, we focus on two research topics: Storage Hierarchy, which explores hierarchical storage management (HSM) in multi-tiered storage systems; and Information Hierarchy, which targets the extraction of intrinsic data hierarchies from raw data.

We begin by introducing the key stages of data life cycle and their associated challenges in the Big Data era, alongside a review of machine learning foundations and their potentials for addressing these challenges. Subsequently, we present the Storage Hierarchy project, which is detailed across Paper I, II, and III. In these works, we develop automated, adaptive, and efficient HSM approaches using reinforcement learning (RL). In Paper I we introduce the HSM-RL framework for managing file-level data migration in hierarchical storage system (HSS). It leverages RL to optimize file placement and temporal difference learning for real-time adaptability. Paper II extends this work to complex real–world scenarios using scientific datasets, exploring the framework’s flexibility, scalability, and effectiveness. Moving to finer granularity, Paper III presents ReStore, an RL-based page-level data migration approach that incorporates the unique characteristics of modern Solid-State Drives (SSDs), such as read/write asymmetry and parallelism.

The Information Hierarchy project focuses on autonomous extraction of implicit data hierarchies from raw, unlabeled data. Presented in Paper IV, we propose InfoHier, a framework that integrates self-supervised learning (SSL) with hierarchical clustering (HC) to uncover latent data representations and hierarchical structures. By jointly training SSL and HC through a dynamic balancing loss, InfoHier ensure that the HC results align with the intrinsic data hierarchy. This method facilitates meaningful and structured information extraction and retrieval. 

Collectively, the Storage Hierarchy and Information Hierarchy projects advance intelligent data management by enabling efficient storage solutions and autonomous information extraction. These contributions pave the foundation for next generation data management systems, addressing the challenges of Big Data with adaptive and scalable solutions.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2025. p. 93
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 2483
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:uu:diva-544718 (URN)978-91-513-2332-9 (ISBN)
Public defence
2025-02-07, Häggsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2025-01-16 Created: 2024-12-08 Last updated: 2025-01-16

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Zhang, TianruHellander, AndreasToor, Salman

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