A new benchmark called LayoutBench has been developed to evaluate the performance of different cloud storage layouts for multimedia machine learning workloads. The benchmark compares three strategies: individual objects (L1), sequential tar archives (L2), and columnar Parquet files (L3). Experiments on ImageNet using AWS S3 and EC2 instances show that L2 offers lower latency through connection reuse, while L3 is fastest for very large retrievals but transfers significantly more data and requires more memory. Data transfer costs were found to be the dominant expense across all layouts. AI
IMPACT Provides insights into optimizing data retrieval costs and performance for large-scale multimedia machine learning datasets in cloud environments.
RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating cloud storage layouts for multimedia data. [lever_c_demoted from research: ic=1 ai=0.7]
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