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

Java编程语言应用广泛，具有重要的商业和经济意义。它之所以受到青睐，部分原因是它具有对所使用的计算机内存资源进行自动管理的特点，简化了程序员的此类管理。Java(和其他托管语言)中的内存管理在成本和效率方面已经达到了平台期，因为大多数当前技术都是基于在程序运行时收集的少量粗粒度度量进行调优的。通过使用对当前和近期内存使用的更准确估计来推动更好的内存管理决策，可能会获得实质性的改进。这将减少运行Java程序所需的时间、内存和能源。这对从小型嵌入式系统到笔记本电脑和台式机再到大型服务器的所有Java应用程序都具有重要意义。因此，迫切需要技术来获得更好的Java内存使用在线预测器。该奖项将支持的研究计划的长期目标是通过使用更好的在线预测器来驱动更复杂的分配器和收集器决策，从而大幅提高内存分配和垃圾回收效率。这个特定项目的目标是开发机器学习技术，以诱导对影响内存管理器性能的Java内存分配特性进行准确和计算高效的预测。例如，预测成为“垃圾”(可以回收和重复使用以供将来分配)的对象的数量，以及将长期使用且不会很快成为垃圾的对象。方法是学习基于从可观察的运行时事件(如对特定方法的调用或特定对象的分配)编译的特征来预测内存使用的模型。学习模型的数据将从详细的程序执行轨迹分析中获得。将选择既能提供有关内存使用情况的信息，又能以较低的空间和时间开销计算的功能。然后，程序将被修改为在运行时计算这些功能，并在程序执行时使用实时预测模型来预测未来的内存使用情况。这些预测将用于提高内存管理性能。这将通过例如改进垃圾收集的定时来实现，从而使其发生在程序执行期间的点上，从而以较低的工作量产生较高的存储器回收。

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.