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CHEOPS 2021 : Workshop on Challenges and Opportunities of Efficient and Performant Storage Systems

Held remotely
Event Date: Apr 26, 2021 - Apr 26, 2021
Abstract Submission Deadline: Feb 10, 2021
Submission Deadline: Feb 17, 2021
Notification of Acceptance: Mar 22, 2021
Camera Ready Version Due : Mar 31, 2021

About

The first workshop on “Challenges and Opportunities of Efficient and Performant Storage Systems” (CHEOPS) is aimed at researchers, developers of scientific applications, engineers and everyone interested in the evolution of storage systems. As the developments of computing power, storage and network technologies continue to diverge, the bandwidth performance gap between them widens. This trend, combined with the ever growing data volumes and data-driven computing such as machine learning, results in I/O and storage limitations, impacting the scalability and efficiency of current and future computing systems. Some of these challenges are quantitative, such as scale to match Exascale system requirements, or latency reduction of the software stack to efficiently integrate new generations of hardware like storage class memory (SCM). Some other issues are more subtle and arise with the increased complexity of the storage solutions, like new smarter and more potent data management tools, monitoring systems or interoperability between I/O components or data formats.

Call For Paper

Submissions may be more hands-on than research papers and we therefore explicitly encourage submissions in the early stages of research. Topics of interest include, but are not limited to:

  • Operating system optimizations
  • Kernel and user space file/storage systems
    • Including virtual file systems
  • Cloud, parallel and distributed file/storage systems
    • Network challenges, such as scalability, QoS and partitionability
  • Approaches for low-latency and heterogeneous storage systems
  • Non-volatile memory technology and integration
  • Metadata management
  • Machine Learning and AI
    • Storage requirements of ML and AI applications
    • Using ML and AI within storage systems (e.g., to replace heuristics)
  • Hybrid solutions using file systems and databases
    • Approaches using query and database interfaces
    • Optimized indexing techniques
  • Data organizations to support online workflows
  • Domain-specific data management solutions
    • Application I/O characterization
  • Storage systems modeling and analysis tools
  • Data reduction techniques such as compression and deduplication
  • Security approaches for storage systems
    • Encryption on different levels of the storage stack
  • UI/UX for storage systems
  • Related experiences from users: what worked, what didn’t?
    • Feedback and empirical evaluation of storage systems

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