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JVM '02 Abstract
Machine Learning for Memory Management
Eva Andreasson, Frank Hoffmann, Olof Lindholm, BEA/Appeal Virtual Machines
Abstract
This article investigates how machine learning methods might enhance
current garbage collection techniques in that they contribute to more adaptive
solutions. Machine learning is concerned with programs that improve with
experience. Machine learning techniques have been successfully applied
to a number of real world problems, such as data mining, game playing,
medical diagnosis, speech recognition and automated control. Reinforcement
learning provides an approach in which an agent interacts with the environment
and learns by trial and error rather than from direct training examples.
In other words, the learning task is specified by rewards and penalties
that indirectly tell the agent what it is supposed to do instead of telling
it how to accomplish the task. In this article we outline a framework for
applying reinforcement learning to optimize the performance of conventional
garbage collectors.
In this project we have researched an adaptive decision process that
makes decisions regarding which garbage collector technique should be invoked
and how it should be applied. The decision is based on information about
the memory allocation behavior of currently running applications. The system
learns through trial and error to take the optimal actions in an initially
unknown environment.
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