The actor model has gained significant popularity in recent years, because its high-level abstraction facilitates the construction of massive concurrent distributed systems, e.g. cloud applications. However, building an efficient actor system is a challenging task. In particular, the concurrency granularity has a critical impact on the performance of actor systems. Moreover, the best granularity depends on both the available hardware and the characteristics of the workload. This paper proposes a new actor system, ElasticActor, that automatically adjusts the granularity of parallel execution. In ElasticActor, all actors are assigned to a number of actor stations. Actors in the same station share a mailbox and constitute the minimum scheduling unit. Therefore, the runtime system can adjust the granularity by changing the number of actor stations in the system. More specifically, we model the average latency and derive a closed-form formula to compute the best number of stations. The experimental results show that ElasticActor outperforms two state-of-the-art actor systems, CAF and Orleans. It gains up to 1.3$$\times $$× throughput and decreases the average latency by 0.75$$\times $$× thanks to the granularity adjustment.

中文翻译：

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ElasticActor：具有自动粒度调整功能的 Actor 系统

演员模型近年来获得了极大的普及，因为它的高级抽象有助于构建大规模并发分布式系统，例如云应用程序。然而，构建一个高效的actor系统是一项具有挑战性的任务。特别是，并发粒度对actor系统的性能有着至关重要的影响。此外，最佳粒度取决于可用硬件和工作负载的特征。本文提出了一种新的actor系统ElasticActor，它可以自动调整并行执行的粒度。在 ElasticActor 中，所有演员都被分配到多个演员站。同一站内的参与者共享一个邮箱，构成最小调度单元。所以，运行时系统可以通过改变系统中actor站的数量来调整粒度。更具体地说，我们对平均延迟进行建模并推导出一个封闭式公式来计算最佳站数。实验结果表明，ElasticActor 的性能优于两个最先进的 Actor 系统，CAF 和 Orleans。由于粒度调整，它获得了高达 1.3$$\times $$× 的吞吐量，并将平均延迟降低了 0.75$$\times $$×。