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The Economics of
Garbage Collection
Jeremy Singer, Richard Jones, Gavin Brown, Mikel Luján
Motivation
The main goals

• Determine allocation curve

• Introduce allocation elasticity

• Make use of introduced elasticity
Microeconomics demand curve
Garbage collection analogy
Empirical Allocation Curves
• Empirical data from real-world Java application
• Different algorithms from MMTk toolkit run on
  JikesRVM
• Java benchmarks from DaCapo suite
• Fixed size heap 35-500 MB
Full-Heap Collection
• Mark-Sweep GC
• Allocation curves obey the law of allocation
Generational Collection
• Generational Mark-Sweep GC
• Nursery space – frequently collected
• Mature space – long-lived objects
• Added weight of 0.1 to Nursery GC
Shifting the Allocation Curve
• Added “tax” – additional words to object header
• Allocation curve shifted upwards
Elasticity
Controlling Heap Growth using Elasticity
• Elasticity to improve GC performance

• Controlling the expansion rate heap

• Default heuristics for controlling heap size

• Sensitivity in default heuristics

• Alternative heuristics
Alternative Heuristic for Heap Growth
Management
• Based on allocation elasticity




• target elasticity, E
   o small E, slow heap growth rate
   o large E, rapid heap growth rate
Execution Time Benchmarks
Final Heap Sizes
Related Work
• Stefanovic et al. used statistical techniques

• Baker offered radioactive decay model

• Garbage collection and transactional memory
Conclusion
• Microeconomics and memory management

• Allocation curve

• Allocation elasticity
The Economics of Garbage Collection: Introducing Allocation Elasticity

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The Economics of Garbage Collection: Introducing Allocation Elasticity