SNC-meister: Admitting more tenants with tail latency SLOs

Timothy Zhu, Daniel S. Berger, Mor Harchol-Balter

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

36 Citations (SciVal)


Meeting tail latency Service Level Objectives (SLOs) in shared cloud networks is both important and challenging. One primary challenge is determining limits on the multitenancy such that SLOs are met. Doing so involves estimating latency, which is difficult, especially when tenants exhibit bursty behavior as is common in production environments. Nevertheless, recent papers in the past two years (Silo, QJump, and PriorityMeister) show techniques for calculating latency based on a branch of mathematical modeling called Deterministic Network Calculus (DNC). The DNC theory is designed for adversarial worst-case conditions, which is sometimes necessary, but is often overly conservative. Typical tenants do not require strict worst-case guarantees, but are only looking for SLOs at lower percentiles (e.g., 99th, 99.9th). This paper describes SNC-Meister, a new admission control system for tail latency SLOs. SNC-Meister improves upon the state-of-the-art DNC-based systems by using a new theory, Stochastic Network Calculus (SNC), which is designed for tail latency percentiles. Focusing on tail latency percentiles, rather than the adversarial worst-case DNC latency, allows SNC-Meister to pack together many more tenants: in experiments with production traces, SNC-Meister supports 75% more tenants than the state-of-the-art.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th ACM Symposium on Cloud Computing, SoCC 2016
EditorsYanlei Diao, Marcos K. Aguilera, Brian Cooper, Yanlei Diao
PublisherAssociation for Computing Machinery, Inc
Number of pages14
ISBN (Electronic)9781450345255
StatePublished - Oct 5 2016
Event7th ACM Symposium on Cloud Computing, SoCC 2016 - Santa Clara, United States
Duration: Oct 5 2016Oct 7 2016

Publication series

NameProceedings of the 7th ACM Symposium on Cloud Computing, SoCC 2016


Other7th ACM Symposium on Cloud Computing, SoCC 2016
Country/TerritoryUnited States
CitySanta Clara

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computational Theory and Mathematics


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