2022 Proceeding- Conference Chairs:
- Michael Kuhn,
- Kira Duwe,
- Jean-Thomas Acquaviva,
- Konstantinos Chasapis,
- Jalil Boukhobza
- Sponsor:
- sigops
The second 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.
The objective of this workshop is to present state-of-the-art research, innovative ideas and experiences that focus on the design and implementation of storage systems in both academic and industrial worlds.
Proceeding Downloads
Analysis and workload characterization of the CERN EOS storage system
Modern, large-scale scientific computing runs on complex exascale storage systems that support even more complex data workloads. Understanding the data access and movement patterns is vital for informing the design of future iterations of existing ...
Data-aware compression for HPC using machine learning
While compression can provide significant storage and cost savings, its use within HPC applications is often only of secondary concern. This is in part due to the inflexibility of existing approaches where a single compression algorithm has to be used ...
TONE: cutting tail-latency in learned indexes
Low memory footprint and tail latency are important in indexing for data management systems. Learned indexes have been gaining popularity in recent years due to their low memory overhead, and adaptability to fluctuations in workloads. However, state-of-...
Understanding the performance of erasure codes in hadoop distributed file system
Replication has been successfully employed and practiced to ensure high data availability in large-scale distributed storage systems. However, with the relentless growth of generated and collected data, replication has become expensive not only in terms ...
SLRL: a simple least remaining lifetime file evicition policy for HPC multi-tier storage systems
HPC systems are composed of multiple tiers of storage, from the top high performance tier (high speed SSDs) to the bottom capacitive one (tapes). File placement in such architecture is managed through prefetchers (bottom-up) and eviction policies (top-...
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Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| CHEOPS '21 | 8 | 6 | 75% |
| Overall | 8 | 6 | 75% |