Garbage Collector Throughput Optimization via Dynamic Parameter Tuning

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Solution Overview

Problem

Existing techniques for tuning generational garbage collectors fail to determine optimal generation sizes and tenuring thresholds that maximize throughput while meeting pause time and footprint goals, leading to suboptimal performance.

Innovation Solution

A system that uses a throughput model to estimate characteristics and adjust tunable parameters, such as heap partition sizes and tenuring thresholds, by numerically evaluating derivatives to maximize throughput, thereby optimizing garbage collection performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If generation sizes and tenuring threshold are adjusted to increase throughput, then garbage collection throughput is improved, but pause time and footprint constraints may be violated

Engineering Contradiction:

Improvegarbage collection throughputVSAvoidpause time and footprint constraints

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system dynamically adjusts generation sizes and tenuring threshold parameters based on monitored characteristics (minor collection time, major collection time, data promotion amounts) to optimize throughput while maintaining constraint compliance through continuous parameter tuning

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system implements a feedback loop where characteristics are monitored, throughput is estimated using a model, and parameters are adjusted accordingly. The feedback mechanism ensures that throughput improvements do not violate pause time or footprint constraints by continuously adapting parameters based on actual performance data

Inventive Principle:

Principle #23Feedback

2Ease of operation

If heuristic policies are used to adjust generation sizes, then pause time and throughput goals can be met, but optimal throughput cannot be determined

Engineering Contradiction:

Improveheuristic policy implementationVSAvoidgarbage collection throughput

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system replaces heuristic-based mechanical adjustment policies with a mathematical model that uses derivatives and optimization algorithms to determine optimal parameters. This substitution enables precise calculation of throughput-maximizing generation sizes and tenuring thresholds rather than relying on rule-of-thumb heuristics

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system transitions from static heuristic policies to dynamic optimization that continuously adapts parameters based on monitored characteristics. The dynamic adjustment mechanism uses real-time data about collection times and data promotion to recalculate optimal parameters, enabling the system to achieve maximum throughput under varying workload conditions

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS8356061B2Maximizing throughput for a garbage collector

Publication Date: 2013.01.15 ORACLE AMERICAN INC

AI summary

Some embodiments of the present invention provide a system that executes a garbage collector in a computing system. During operation, the system obtains a throughput model for the garbage collector and estimates a set of characteristics associated with the garbage collector. Next, the system applies the characteristics to the throughput model to estimate a throughput of the garbage collector. The system then determines a level of performance for the garbage collector based on the estimated throughput. Finally, the system adjusts a tunable parameter for the garbage collector based on the level of performance to increase the throughput of the garbage collector.