Overcoming data locality: An in-memory runtime file system with symmetrical data distribution
We present a locality-agnostic in-memory storage system for many-task computing. Locality-aware approaches introduce storage and network communication imbalances. Our approach performs better on both micro-benchmarks and real-world applications. Evaluation shows that our approach is applicable on both clusters, and clouds.
many-task computing in-memory file system distributed hashing scalability