article

NG2C: pretenuring garbage collection with dynamic generations for HotSpot big data applications

Authors:

Luís Picciochi Oliveira,

Paulo FerreiraAuthors Info & Claims

ACM SIGPLAN Notices, Volume 52, Issue 9

Pages 2 - 13

https://doi.org/10.1145/3156685.3092272

Published: 18 June 2017 Publication History

Abstract

Big Data applications suffer from unpredictable and unacceptably high pause times due to Garbage Collection (GC). This is the case in latency-sensitive applications such as on-line credit-card fraud detection, graph-based computing for analysis on social networks, etc. Such pauses compromise latency requirements of the whole application stack and result from applications' aggressive buffering/caching of data, exposing an ill-suited GC design, which assumes that most objects will die young and does not consider that applications hold large amounts of middle-lived data in memory.

To avoid such pauses, we propose NG2C, a new GC algorithm that combines pretenuring with user-defined dynamic generations. By being able to allocate objects into different generations, NG2C is able to group objects with similar lifetime profiles in the same generation. By allocating objects with similar lifetime profiles close to each other, i.e. in the same generation, we avoid object promotion (copying between generations) and heap fragmentation (which leads to heap compactions) both responsible for most of the duration of HotSpot GC pause times.

NG2C is implemented for the OpenJDK 8 HotSpot Java Virtual Machine, as an extension of the Garbage First GC. We evaluate NG2C using Cassandra, Lucene, and GraphChi with three different GCs: Garbage First (G1), Concurrent Mark Sweep (CMS), and NG2C. Results show that NG2C decreases the worst observable GC pause time by up to 94.8% for Cassandra, 85.0% for Lucene and 96.45% for GraphChi, when compared to current collectors (G1 and CMS). In addition, NG2c has no negative impact on application throughput or memory usage.

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Index Terms

NG2C: pretenuring garbage collection with dynamic generations for HotSpot big data applications
1. Software and its engineering
  1. Software organization and properties
    1. Contextual software domains
      1. Operating systems
        Memory management
        Garbage collection

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cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 52, Issue 9

ISMM '17

September 2017

127 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/3156685

Editor:
Matthew Fluet

Issue’s Table of Contents

ISMM 2017: Proceedings of the 2017 ACM SIGPLAN International Symposium on Memory Management
June 2017
127 pages
ISBN:9781450350440
DOI:10.1145/3092255
General Chair:
Christoph M. Kirsch
University of Salzburg, Austria
,
Program Chair:
Ben L. Titzer
Google, Germany

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Published: 18 June 2017

Published in SIGPLAN Volume 52, Issue 9

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https://dl.acm.org/doi/10.1145/3627703.3629579
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