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GC-Wise: A Self-adaptive approach for memory-performance efficiency in Java VMs

机译：GC-Wise：一种自适应方法，可提高Java VM中的内存性能效率

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High-level language runtimes are ubiquitous in every cloud deployment. From the geo-distributed heavy resources cloud provider to the new Fog and Edge deployment paradigms, all rely on these runtimes for portability, isolation and resource management. Across these clouds, an efficient resource management of several managed runtimes involves limiting the heap size of some VMs so that extra memory can be assigned to higher priority workloads. The challenges in this approach rely on the potential scale of systems and the need to make decisions in an application-driven way, because performance degradation can be severe, and therefore it should be minimized. Also, each tenant tends to repeat the execution of applications with similar memory-usage patterns, giving opportunity to reuse parameters known to work well for a given workload.This paper presents GC-Wise, a system to determine, at run-time, the best values for critical heap management parameters of the OpenJDK JVM, aiming to maximize memory-performance efficiency. GC-Wise comprises two main phases: 1) a training phase where it collects, with different heap resizing policies, representative execution metrics during the lifespan of a workload; and 2) an execution phase where an oracle matches the execution parameters of new workloads against those of already seen workloads, and enforces the best heap resizing policy. Distinctly from other works, the oracle can also decide upon unknown workloads. Using representative applications and different hardware setting (a resourceful server and a fog-like device), we show that our approach can lead to significant memory savings with low-impact on the throughput of applications. Furthermore, we show that we can predict with high accuracy the best heap resizing configuration in a relatively short period of time. (C) 2019 Published by Elsevier B.V.
机译：高级语言运行时在每个云部署中无处不在。从地理分布的重资源云提供商到新的Fog和Edge部署范例，所有这些都依赖于这些运行时来实现可移植性，隔离性和资源管理。在这些云之间，对多个托管运行时的有效资源管理涉及限制某些VM的堆大小，以便可以将额外的内存分配给优先级更高的工作负载。这种方法所面临的挑战取决于系统的潜在规模以及以应用程序驱动方式进行决策的需求，因为性能下降可能很严重，因此应将其降到最低。此外，每个租户都倾向于以相似的内存使用模式重复执行应用程序，从而有机会重用已知的参数以在给定的工作负载下正常工作。 OpenJDK JVM的关键堆管理参数的最佳值，旨在最大程度地提高内存性能。 GC-Wise包含两个主要阶段：1）训练阶段，在该阶段，它使用不同的堆大小调整策略收集工作负载生命周期中的代表性执行指标； 2）执行阶段，其中oracle将新工作负载的执行参数与已经看到的工作负载的执行参数进行匹配，并实施最佳的堆大小调整策略。与其他工作截然不同，oracle也可以决定未知的工作负载。通过使用具有代表性的应用程序和不同的硬件设置（资源丰富的服务器和类似雾的设备），我们证明了我们的方法可以节省大量内存，并且对应用程序吞吐量的影响较小。此外，我们表明，我们可以在相对较短的时间内高精度地预测最佳堆大小调整配置。 （C）2019由Elsevier B.V.发布

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