Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo


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 stream-ing workloads have to meet latency targets and avoid breach-ing service-level agreements, existing solutions are in capable 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 2.7× in single tenant settings, ii) reduces query latency by 4.6× in multi-tenant scenarios, and iii) weathers transient spikes of workload.

In USENIX Symposium on Networked Systems Design and Implementation (NSDI)
Luo Mai
Luo Mai
Assistant Professor

My research interests include computer systems, machine learning and data management.