SmallTail: Scaling cores and probabilistic cloning requests for web systems

Ewnetu Bayuh Lakew, Robert Birke, Juan F. Pérez, Erik Elmroth, Lydia Y. Chen

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

    2 Scopus citations

    Abstract

    Users quality of experience on web systems are largely determined by the tail latency, e.g., 95th percentile. Scaling resources along, e.g., the number of virtual cores per VM, is shown to be effective to meet the average latency but falls short in taming the latency tail in the cloud where the performance variability is higher. The prior art shows the prominence of increasing the request redundancy to curtail the latency either in the off-line setting or without scaling-in cores of virtual machines. In this paper, we propose an opportunistic scaler, termed SmallTail, which aims to achieve stringent targets of tail latency while provisioning a minimum amount of resources and keeping them well utilized. Against dynamic workloads, SmallTail simultaneously adjusts the core provisioning per VM and probabilistically replicates requests so as to achieve the tail latency target. The core of SmallTail is a two level controller, where the outer loops controls the core provision per distributed VMs and the inner loop controls the clones in a finer granularity. We also provide theoretical analysis on the steady-state latency for a given probabilistic replication that clones one out of N arriving requests. We extensively evaluate SmallTail on three different web systems, namely web commerce, web searching, and web bulletin board. Our testbed results show that SmallTail can ensure the 95th latency below 1000 ms using up to 53% less cores compared to the strategy of constant cloning, whereas scaling-core only solution exceeds the latency target by up to 70%.

    Original languageEnglish (US)
    Title of host publicationProceedings - 15th IEEE International Conference on Autonomic Computing, ICAC 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages31-40
    Number of pages10
    ISBN (Electronic)9781538651391
    DOIs
    StatePublished - Oct 18 2018
    Event15th IEEE International Conference on Autonomic Computing, ICAC 2018 - Trento, Italy
    Duration: Sep 3 2018Sep 7 2018

    Publication series

    NameProceedings - 15th IEEE International Conference on Autonomic Computing, ICAC 2018

    Conference

    Conference15th IEEE International Conference on Autonomic Computing, ICAC 2018
    CountryItaly
    CityTrento
    Period9/3/189/7/18

    All Science Journal Classification (ASJC) codes

    • Computer Networks and Communications
    • Computer Science Applications
    • Safety, Risk, Reliability and Quality
    • Control and Optimization

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