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Lifetime-Based Memory Management for Distributed Data Processing Systems

Summary: Proposes a lifetime-based memory manager that analyzes user data/types to predict lifetimes and allocate/release memory, reducing GC pressure in distributed processing. Deca on Spark groups same-lifetime objects into byte arrays and frees them at end-of-life, delivering up to 99.9% GC reduction, up to 41.6x speedups, and ~46% memory savings. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11380
Venue
VLDB
Year
2016
Pagerank
5.3258796e-05
Overall Rank
5,793 | 59.71%
DOI
-

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
66 Spark SQL: Relational Data Processing in Spark 2015 SIGMOD 0.00061639801
2,476 A Platform for Scalable One-Pass Analytics using MapReduce 2011 SIGMOD 8.6960139e-05
3,504 M3R: Increased Performance for In-Memory Hadoop Jobs 2012 VLDB 7.0347515e-05
4,437 Clash of the Titans: MapReduce vs. Spark for Large Scale Data Analytics 2015 VLDB 6.1907793e-05
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