• DOI:10.1007/978-3-319-69459-7_4
• Corpus ID: 2037130

Locality-Aware GC Optimisations for Big Data Workloads

@inproceedings{Patrcio2017LocalityAwareGO,
  title={Locality-Aware GC Optimisations for Big Data Workloads},
  author={Duarte Patr{\'i}cio and Rodrigo Bruno and Jos{\'e} Sim{\~a}o and Paulo Ferreira and Lu{\'i}s Veiga},
  booktitle={OTM Conferences},
  year={2017}
}

• D. Patrício, Rodrigo Bruno, +2 authors L. Veiga
• Published in OTM Conferences 23 October 2017
• Computer Science

Many Big Data analytics and IoT scenarios rely on fast and non-relational storage (NoSQL) to help processing massive amounts of data. In addition, managed runtimes (e.g. JVM) are now widely used to support the execution of these NoSQL storage solutions, particularly when dealing with Big Data key-value store-driven applications. The benefits of such runtimes can however be limited by automatic memory management, i.e., Garbage Collection (GC), which does not consider object locality, resulting in objects that point to each other being dispersed in memory. In the long run this may break the service-level of applications due to extra page faults and degradation of locality on system-level memory caches. We propose, LAG1 (short for Locality-Aware G1), an extension of modern heap layouts to promote locality between groups of related objects. This is done with no previous application profiling and in a way that is transparent to the programmer, without requiring changes to existing code. The heap layout and algorithmic extensions are implemented on top of the Garbage First (G1) garbage collector (the new by-default collector) of the HotSpot JVM. Using the YCSB benchmarking tool to benchmark HBase, a well-known and widely used Big Data application, we show negligible overhead in frequent operations such as the allocation of new objects, and significant improvements when accessing data, supported by higher hits in system-level memory structures.