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Resource provisioning in multi-tenant stream processing systems faces thedual challenges of keeping resource utilization high (withoutover-provisioning), and ensuring performance isolation. In our commonproduction use cases, where streaming workloads have to meet latency targetsand avoid breaching service-level agreements, existing solutions are incapableof handling the wide variability of user needs. Our framework called Cameo usesfine-grained stream processing (inspired by actor computation models), and isable to provide high resource utilization while meeting latency targets. Cameodynamically calculates and propagates priorities of events based on userlatency targets and query semantics. Experiments on Microsoft Azure show thatcompared to state-of-the-art, the Cameo framework: i) reduces query latency by2.7X in single tenant settings, ii) reduces query latency by 4.6X inmulti-tenant scenarios, and iii) weathers transient spikes of workload.

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