Blaze: Holistic Caching for Iterative Data Processing
Won Wook Song, Jeongyoon Eo, Taegeon Um, Myeongjae Jeon, Byung-Gon Chun · 2024
Modern data processing workloads, such as machine learning and graph processing, involve iterative computations to converge generated models into higher accuracy. An effective caching mechanism is vital to expedite iterative computations since the intermediate data that needs to be stored in memory grows larger over iterations, often exceeding the memory capacity. However, existing systems handle intermediate data through separate operational layers (e.g., caching, eviction, and recovery), with each layer working independently in a greedy or cost-agnostic manner. These layers typically rely on user annotations and past access patterns, failing to make globally optimal decisions for the workload.