On the Implications of Heterogeneous Memory Tiering on Spark In-Memory Analytics

This study considers a multi-tier heterogeneous DRAM/NVM memory system with contrasting access latency, bandwidth and energy consumption capabilities. The paper examines the implications of heterogeneous memory tiering on Spark in-memory analytics and addresses the challenge of the growing demand for efficient and scalable computing solutions that can handle the massive amounts of data generated by modern applications.