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)
Incoming Non-self Citations Over Time
Authors
- 1. Lu Lu
- 2. Xuanhua Shi
- 3. Yongluan Zhou
- 4. Xiong Zhang
- 5. Hai Jin
- 6. Cheng Pei
- 7. Ligang He
- 8. Yuanzhen Geng
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,686 | Concurrent Log-Structured Memory for Many-Core Key-Value Stores | 2018 | VLDB | 4.675046e-05 |
| 8,005 | Pangea: Monolithic Distributed Storage for Data Analytics | 2019 | VLDB | 4.6044087e-05 |
| 9,337 | PlinyCompute: A Platform for High-Performance, Distributed, Data-Intensive Tool Development | 2018 | SIGMOD | 4.351469e-05 |
| 9,912 | Chukonu: A Fully-Featured High-Performance Big Data Framework that Integrates a Native Compute Engine into Spark | 2022 | VLDB | 4.2524493e-05 |
| 11,699 | An Experimental Evaluation of Garbage Collectors on Big Data Applications | 2019 | VLDB | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
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 | 0.00061707583 |
| 2,476 | A Platform for Scalable One-Pass Analytics using MapReduce | 2011 | SIGMOD | 8.6907971e-05 |
| 3,511 | M3R: Increased Performance for In-Memory Hadoop Jobs | 2012 | VLDB | 7.0242945e-05 |
| 4,435 | Clash of the Titans: MapReduce vs. Spark for Large Scale Data Analytics | 2015 | VLDB | 6.18493e-05 |
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| 8,454 | Towards Resource Efficiency: Practical Insights into Large-Scale Spark Workloads at ByteDance | 2024 | VLDB | 4.5022073e-05 |
| 4,807 | Resource Elasticity for Large-Scale Machine Learning | 2015 | SIGMOD | 5.9045148e-05 |
| 2,094 | Maintaining Time-Decaying Stream Aggregates | 2003 | PODS | 9.5556069e-05 |
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