SHF:Small: Accurate and Computationally Efficient Predictors of Java Memory Resource Consumption

SHF:Small：Java 内存资源消耗的准确且计算高效的预测器

• 批准号: 1320498
• 负责人: J. Eliot Moss
• 金额: $ 45万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320498&HistoricalAwards=false
• 关键词: SHF; Small; Accurate; Computationally; Efficient

The Java programming language is widely-used and of great commercial and economic significance. It is favored in part because it features automatic management of the computer memory resources it uses, simplifying such management for the programmer. Memory management in Java (and other managed languages) has reached a plateau in cost and effectiveness because most current techniques are tuned based on a small number of coarse-grained measures gathered while programs run. Substantial improvement might be gained from using more accurate estimation of current and near-future memory use to drive better memory management decisions. This would reduce the time, memory, and energy requirements to run Java programs. This is of significance to the full range of Java applications from small embedded systems through laptops and desktops to large servers. There is therefore an urgent need for techniques to derive better online predictors of Java memory use.The long-term goal of the research program this award will support is to substantially improve memory allocation and garbage collection effectiveness by using better online predictors to drive more sophisticated allocator and collector decisions. The objective of this particular project is to develop machine learning techniques that induce accurate and computationally efficient predictors of characteristics of Java memory allocation that influence memory manager performance. Examples include predicting the volume of objects that become "garbage" (can be reclaimed and reused for future allocations), as well as objects that will be in use for a long time and will not become garbage soon. The approach is to learn models that predict memory usage based on features compiled from observable run-time events like calls to particular methods or allocations of certain objects. Data to learn models will be obtained from analysis of detailed program execution traces. Features will be selected that are both informative of memory use and computable with low space and time overheads. Programs will then be modified to compute these features as they run, and real-time predictive models will be used to predict future memory usage as programs execute. These predictions will be used to improve memory management performance. This will be accomplished by, for example, improving the timing of garbage collection so that it occurs at points during program execution that result in higher memory reclamation with lower effort.

Java编程语言广泛使用，并且具有巨大的商业和经济意义。它之所以受到青睐，是因为它具有自动管理其使用的计算机内存资源的管理，从而简化了程序员的此类管理。 Java（以及其他托管语言）中的内存管理已经达到了成本和有效性的平稳，因为大多数当前技术都是根据在程序运行时收集的少量粗粒措施来调整的。 通过使用更准确的当前和近距离内存使用来推动更好的内存管理决策，可以从更准确的估计中获得实质性的改进。 这将减少运行Java程序的时间，内存和能量需求。这对于从小型嵌入式系统通过笔记本电脑和台式机到大型服务器的全部Java应用具有重要意义。 因此，迫切需要技术来获得更好的Java内存使用的在线预测指标。研究计划的长期目标该奖项将支持通过使用更好的在线预测指标来推动更复杂的分配者和收藏家的决策，从而实质上提高内存分配和垃圾收集效果。 该特定项目的目的是开发机器学习技术，以诱导影响内存管理器性能的Java内存分配特征的准确和计算有效的预测指标。 示例包括预测变成“垃圾”的物体的数量（可以回收并重新使用以供将来的分配），以及将长时间使用的对象，并且不会很快变成垃圾。 该方法是学习基于可观察到的运行时事件（例如调用特定方法或某些对象分配的呼叫）的功能来预测内存使用的模型。要学习模型的数据将从分析的详细程序执行跟踪中获得。将选择具有内存使用信息的功能，并且可以计算低空间和时间开销。 然后，将修改程序以计算这些功能在运行时计算它们，并将使用实时预测模型来预测未来的内存使用情况，以执行程序。 这些预测将用于改善内存管理绩效。 例如，通过改善垃圾收集的时间来实现这一目标，以便在程序执行过程中发生在较高的内存填海过程中。