Rising Stars 2020:

Le Xu

PhD Candidate

University of Illinois Urbana Champaign

Areas of Interest

  • Distributed Systems and Cloud Computing
  • Database Management Systems
  • Operating Systems and Networking


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 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.


Le Xu is a Ph.D. student of Computer Science at the University of Illinois at Urbana Champaign. I work with Prof. Indranil Gupta and Distributed Protocols Research Group (DPRG). Her research spans across all aspects of distributed systems and applications. Her current research focuses on resource provisioning that improves system elasticity and building a resource-efficient cloud stack that provides predictable performance.

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