Optimizing Data Placement on Hierarchical Storage Architecture via Machine Learning

  • Peng Cheng
  • Yutong LuEmail author
  • Yunfei Du
  • Zhiguang Chen
  • Yang Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11783)


As storage hierarchies are getting deeper on modern high-performance computing systems, intelligent data placement strategies that can choose the optimal storage tier dynamically is the key to realize the potential of hierarchical storage architecture. However, providing a general solution that can be applied in different storage architectures and diverse applications is challenging. In this paper, we propose adaptive storage learner (ASL), which explores the idea of using machine learning techniques to mine the relationship between data placement strategies and I/O performance under varied workflow characteristics and system status, and uses the learned model to choose the optimal storage tier intelligently. We implement a prototype and integrate it into an existing data management system. Empirical comparison based on real scientific workflows tests shows that ASL is capable of combining workflow characteristics and real-time system status to make optimal data placement decisions.


Storage optimization Machine learning Hierarchical storage Data placement 



This work was supported by the National Key R&D Program of China under Grant No. 2017YFB0202204 and No. 2017YFB0202201, the National Science Foundation of China under Grant NO.U1811464, and the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant NO. 2016ZT06D211.


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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Peng Cheng
    • 1
    • 2
  • Yutong Lu
    • 3
    Email author
  • Yunfei Du
    • 3
  • Zhiguang Chen
    • 3
  • Yang Liu
    • 4
  1. 1.College of Computer, National University of Defense TechnologyChangshaChina
  2. 2.State Key Laboratory of High Performance ComputingChangshaChina
  3. 3.National Supercomputer Center in Guangzhou, School of Data and Computer ScienceSun Yat-Sen UniversityGuangzhouChina
  4. 4.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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