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 and storage with various characteristics and features have become available. Most of these are highly efficient, but ultimately create data silos. It becomes difficult to move and work coherently with data as new requirements emerge as no single framework can efficiently fulfill the data management needs of diverse applications. A possible solution is to design smart and efficient hierarchical (multi-tier) storage solutions. A hierarchical storage system (HSS) is a meta solution that consists of different storage frameworks organized as a jointly constructed large storage pool. It brings a number of benefits including better utilization of the storage, cost-efficiency, and use of different features provided by the underlying storage frameworks. In order to maximize the gains of hierarchical storage solutions, it is important that they include intelligent and autonomous mechanisms for data management grounded in the features of the different underlying frameworks. These decisions should be made according to the characteristics of the dataset, tier status, and access patterns. These are highly dynamic parameters and defining a policy based on the mentioned parameters is a non-trivial task. 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 an implementation 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 compared to the dynamic rule-based policies.
ReinFog: A Deep Reinforcement Learning Empowered Framework for Resource Management in Edge and Cloud Computing Environments
The Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算与信息学系量子云计算与分布式系统（qCLOUDS）实验室）；The Faculty of Information Technology, Monash University（莫纳什大学信息技术学院）
Disk storage management for LHCb based on Data Popularity estimator
Yandex School of Data Analysis（Yandex数据分析学院）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；CERN - European Organization for Nuclear Research（欧洲核子研究组织）；National Research University Higher School of Economics (HSE)（国立研究型大学高等经济学院）
