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 language | English |
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Title of host publication | Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation |
Publisher | USENIX Association |
ISBN (Print) | 978-1-939133-21-2 |
Publication status | Published - 12 Apr 2021 |
Event | 18th USENIX Symposium on Networked Systems Design and Implementation - Boston, United States Duration: 12 Apr 2021 → 14 Apr 2021 https://www.usenix.org/conference/nsdi21 |
Symposium
Symposium | 18th USENIX Symposium on Networked Systems Design and Implementation |
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Abbreviated title | NSDI '21 |
Country/Territory | United States |
City | Boston |
Period | 12/04/21 → 14/04/21 |
Internet address |
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Luo Mai
- School of Informatics - Lecturer in Data Centric Systems
- Institute for Computing Systems Architecture
- Computer Systems
Person: Academic: Research Active