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Optimized Memory Management for a Java-Based Distributed In-memory System

机译:针对基于Java的分布式内存系统的优化内存管理

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Several Java-based distributed in-memory systems have been proposed in the literature, but most are not aiming at graph applications having highly concurrent and irregular access patterns to many small data objects. DXRAM is addressing these challenges and relies on DXMem for memory management and concurrency control on each server. DXMem is published as an open-source library, which can be used by any other system, too. In this paper, we briefly describe our previously published but relevant design aspects of the memory management. However, the main contributions of this paper are the new extensions, optimizations, and evaluations. These contributions include an improved address translation which is now faster compared to the old solution with a translation cache. The coarse-grained concurrency control of our first approach has been replaced by a very efficient per-object read-write lock which allows a much better throughput, especially under high concurrency. Finally, we compared DXRAM for the first time to Hazelcast and Infinispan, two state-of-the-art Java-based distributed cache systems using real-world application-workloads and the Yahoo! Cloud Serving Benchmark in a distributed environment. The results of the experiments show that DXRAM outperforms both systems while having a much lower metadata overhead for many small data objects.
机译:文献中已经提出了几种基于Java的分布式内存系统,但是大多数系统并不针对具有对许多小数据对象的高度并发和不规则访问模式的图形应用程序。 DXRAM正在解决这些挑战,并依靠DXMem在每台服务器上进行内存管理和并发控制。 DXMem作为开源库发布,也可以由任何其他系统使用。在本文中,我们简要描述了我们先前发布的但相关的内存管理设计方面。但是,本文的主要贡献是新的扩展,优化和评估。这些贡献包括改进的地址翻译,与具有翻译缓存的旧解决方案相比,现在地址翻译速度更快。我们第一种方法的粗粒度并发控制已被非常有效的每个对象读写锁定所取代,这可以实现更高的吞吐量,尤其是在高并发情况下。最后,我们首次将DXRAM与Hazelcast和Infinispan进行了比较,后者是两个使用实际应用程序工作负载和Yahoo!的基于Java的最先进的分布式缓存系统。分布式环境中的云服务基准。实验结果表明,DXRAM的性能优于两个系统,同时对于许多小型数据对象而言,元数据的开销要低得多。

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