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ZygOS: Achieving Low Tail Latency for Microsecond-scale Networked Tasks

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Edouard BugnionAuthors Info & Claims

SOSP '17: Proceedings of the 26th Symposium on Operating Systems Principles

Pages 325 - 341

https://doi.org/10.1145/3132747.3132780

Published: 14 October 2017 Publication History

Abstract

This paper focuses on the efficient scheduling on multicore systems of very fine-grain networked tasks, which are the typical building block of online data-intensive applications. The explicit goal is to deliver high throughput (millions of remote procedure calls per second) for tail latency service-level objectives that are a small multiple of the task size.

We present ZYGOS, a system optimized for μs-scale, in-memory computing on multicore servers. It implements a work-conserving scheduler within a specialized operating system designed for high request rates and a large number of network connections. ZYGOS uses a combination of shared-memory data structures, multi-queue NICs, and inter-processor interrupts to rebalance work across cores.

For an aggressive service-level objective expressed at the 99th percentile, ZYGOS achieves 75% of the maximum possible load determined by a theoretical, zero-overhead model (centralized queueing with FCFS) for 10μs tasks, and 88% for 25μs tasks.

We evaluate ZYGOS with a networked version of Silo, a state-of-the-art in-memory transactional database, running TPC-C. For a service-level objective of 1000μs latency at the 99th percentile, ZYGOS can deliver a 1.63x speedup over Linux (because of its dataplane architecture) and a 1.26x speedup over IX, a state-of-the-art dataplane (because of its work-conserving scheduler).

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cover image ACM Conferences

SOSP '17: Proceedings of the 26th Symposium on Operating Systems Principles

October 2017

677 pages

ISBN:9781450350853

DOI:10.1145/3132747

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Published: 14 October 2017

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https://doi.org/10.1360/SSI-2024-0339
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