Generational techniques have been very successful in reducing the impact of garbage collection algorithms upon the performance of programs. However, it is impossible for designers of collection algorithms to anticipate the memory allocation behavior of all applications in advance. Existing generational collectors rely upon the applications programmer to tune the behavior of the collector to achieve maximum performance for each application. Unfortunately, because the many tuning parameters require detailed knowledge of both the collection algorithm and the program allocation behavior in order to be used effectively, such tuning is difficult and error prone. We propose a new garbage collection algorithm that uses just two easily understood tuning parameters that directly reflect the maximum memory and pause time constraints familiar to application programmers and users.
Like generational collectors, ours divides memory into two spaces, one for short-lived, and another for long-lived objects. Unlike previous work, our collector dynamically adjusts the boundary between these two spaces in order to directly meet the resource constraints specified by the user. We describe two methods for adjusting this boundary, compare them with several existing algorithms, and show how effectively ours meets the specified constraints. Our pause time collector saved memory by holding median pause times closer to the constraint than the other pause time constrained algorithm and, when not over-constrained, our memory constrained collector exhibited the lowest CPU overhead of the algorithms we measured yet was capable of maintaining a maximum memory constraint.