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

Le Xu, Shivaram Venkataraman, Indranil Gupta, Luo Mai, Rahul Potharaju

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 3X in single-tenant settings, ii) reduces query latency by 5X in multi-tenant scenarios, and iii) weathers transient spikes of workload.
Original languageEnglish
Title of host publication18th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2020
Subtitle of host publicationBoston, MA, USA, April 12-14, 2021
Publication statusAccepted/In press - 2 Jul 2020
Event18th USENIX Symposium on Networked Systems Design and Implementation - Boston, United States
Duration: 12 Apr 202114 Apr 2021
https://www.usenix.org/conference/nsdi21

Symposium

Symposium18th USENIX Symposium on Networked Systems Design and Implementation
Abbreviated titleNSDI '21
CountryUnited States
CityBoston
Period12/04/2114/04/21
Internet address

Fingerprint Dive into the research topics of 'Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo'. Together they form a unique fingerprint.

Cite this