AutoBurst: Autoscaling Burstable Instances for Cost-effective Latency SLOs

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

Abstract

Burstable instances provide a low-cost option for consumers using the public cloud, but they come with significant resource limitations. They can be viewed as "fractional instances"where one receives a fraction of the compute and memory capacity at a fraction of the cost of regular instances. The fractional compute is achieved via rate limiting, where a unique characteristic of the rate limiting is that it allows for the CPU to burst to 100% utilization for limited periods of time. Prior research has shown how this ability to burst can be used to serve specific roles such as a cache backup and handling flash crowds. Our work provides a general-purpose approach to meeting latency SLOs via this burst capability while optimizing for cost. AutoBurst is able to achieve this by controlling both the number of burstable and regular instances along with how/when they are used. Evaluations show that our system is able to reduce cost by up to 25% over the state-of-the-art while maintaining latency SLOs.

Original languageEnglish (US)
Title of host publicationSoCC 2024 - Proceedings of the 2024 ACM Symposium on Cloud Computing
PublisherAssociation for Computing Machinery, Inc
Pages243-258
Number of pages16
ISBN (Electronic)9798400712869
DOIs
StatePublished - Nov 20 2024
Event15th Annual ACM Symposium on Cloud Computing, SoCC 2024 - Redmond, United States
Duration: Nov 20 2024Nov 22 2024

Publication series

NameSoCC 2024 - Proceedings of the 2024 ACM Symposium on Cloud Computing

Conference

Conference15th Annual ACM Symposium on Cloud Computing, SoCC 2024
Country/TerritoryUnited States
CityRedmond
Period11/20/2411/22/24

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computer Networks and Communications
  • Computer Science Applications

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