WorkloadCompactor: Reducing datacenter cost while providing tail latency SLO guarantees

Timothy Zhu, Michael A. Kozuch, Mor Harchol-Balter

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

    18 Citations (SciVal)


    Service providers want to reduce datacenter costs by consolidating workloads onto fewer servers. At the same time, customers have performance goals, such as meeting tail latency Service Level Objectives (SLOs). Consolidating workloads while meeting tail latency goals is challenging, especially since workloads in production environments are often bursty. To limit the congestion when consolidating workloads, customers and service providers often agree upon rate limits. Ideally, rate limits are chosen to maximize the number of workloads that can be co-located while meeting each workload's SLO. In reality, neither the service provider nor customer knows how to choose rate limits. Customers end up selecting rate limits on their own in some ad hoc fashion, and service providers are left to optimize given the chosen rate limits. This paper describes WorkloadCompactor, a new system that uses workload traces to automatically choose rate limits simultaneously with selecting onto which server to place workloads. Our system meets customer tail latency SLOs while minimizing datacenter resource costs. Our experiments show that by optimizing the choice of rate limits, WorkloadCompactor reduces the number of required servers by 30-60% as compared to state-of-the-art approaches.

    Original languageEnglish (US)
    Title of host publicationSoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing
    PublisherAssociation for Computing Machinery, Inc
    Number of pages13
    ISBN (Electronic)9781450350280
    StatePublished - Sep 24 2017
    Event2017 Symposium on Cloud Computing, SoCC 2017 - Santa Clara, United States
    Duration: Sep 24 2017Sep 27 2017

    Publication series

    NameSoCC 2017 - Proceedings of the 2017 Symposium on Cloud Computing


    Other2017 Symposium on Cloud Computing, SoCC 2017
    Country/TerritoryUnited States
    CitySanta Clara

    All Science Journal Classification (ASJC) codes

    • Computational Theory and Mathematics
    • Theoretical Computer Science


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