论文标题
快速移动并满足截止日期：与客串相关的细粒度实时流处理
Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo
论文作者
论文摘要
多租户流处理系统中的资源提供面临的双重挑战是保持资源较高（无需过度提供）并确保绩效隔离。在我们常见的生产用例中，流媒体工作负载必须满足延迟目标并避免违反服务级别的协议，现有的解决方案无法处理用户需求的广泛可变性。我们称为客串的框架使用细粒流处理（受演员计算模型的启发），并且在满足延迟目标时能够提供高资源利用率。客串根据用户延迟目标和查询语义动态计算并传播事件的优先级。 Microsoft Azure上的实验显示，与最先进的客串框架相比：i）在单个租户设置中将查询潜伏期降低2.7倍，ii）在多租户方案中将查询潜伏期降低了4.6倍，而III）则是工作负载的瞬态尖峰。
Resource provisioning in multi-tenant stream processing systems faces the dual challenges of keeping resource utilization high (without over-provisioning), and ensuring performance isolation. In our common production use cases, where streaming workloads have to meet latency targets and avoid breaching service-level agreements, existing solutions are incapable of handling the wide variability of user needs. Our framework called Cameo uses fine-grained stream processing (inspired by actor computation models), and is able to provide high resource utilization while meeting latency targets. Cameo dynamically calculates and propagates priorities of events based on user latency targets and query semantics. Experiments on Microsoft Azure show that compared to state-of-the-art, the Cameo framework: i) reduces query latency by 2.7X in single tenant settings, ii) reduces query latency by 4.6X in multi-tenant scenarios, and iii) weathers transient spikes of workload.
