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DOI

Simplifying the task of resource management and scheduling for customers, while still delivering complex Quality-of-Service (QoS), is key to cloud computing. Many autoscaling policies have been proposed in the past decade to decide on behalf of cloud customers when and how to provision resources to a cloud application utilizing cloud elasticity features. However, in prior work, when a new policy is proposed, it is seldom compared to the state-of-the-art, and is
often compared only to static provisioning using a predefined QoS target. This reduces the ability of cloud customers and of cloud operators to choose and deploy an autoscaling policy. In our work, we conduct an experimental performance evaluation of autoscaling policies, using as application model workflows, a commonly used formalism for automating resource management for applications with well-defined yet complex structure. We present a detailed
comparative study of general state-of-the-art autoscaling policies, along with two new workflow-specific policies. To understand the performance differences between the 7 policies, we conduct various forms of pairwise and group comparisons. We report both individual and aggregated metrics. Our results highlight the trade-offs between the suggested policies, and thus enable a better understanding of the current state-of-the-art.
Original languageEnglish
Title of host publicationProceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, ICPE 2017
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages75-86
Number of pages12
ISBN (Electronic)978-1-4503-4404-3
DOIs
StatePublished - 2017
EventICPE 2017 - L'Aquila, Italy
Duration: 22 Apr 201727 Apr 2017
Conference number: 8
https://icpe2017.spec.org/

Conference

ConferenceICPE 2017
CountryItaly
CityL'Aquila
Period22/04/1727/04/17
Internet address

ID: 29617131