DOI

Resource contention is one of the major problems in cloud datacenters. Many types of resource contention occur, with important impact on the performance and sometimes even the reliability of applications running in cloud datacenters. Cloud applications run together on the same physical machines with different workloads resulting in non-synchronized accesses to the shared resources. This leads to cases where co-hosted applications are contending for the common resources and not receiving the demanded resource amounts. In this work, we investigate the contention in CPU resources, as CPU is allowed to be over-committed by typical SLAs. We propose a CPU-contention predictor for the demanding business-critical workloads, which require low resource contention to deliver the required performance to customers. Our predictor is based on a set of regression models and metrics which we evaluate extensively. We tune the predictor with data collected from a real-world cloud operation spanning multiple datacenters and servicing business-critical workloads.

Original languageEnglish
Title of host publication2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)
Subtitle of host publicationProceedings
Place of PublicationPiscataway
PublisherIEEE
Pages56-61
Number of pages6
ISBN (Electronic)9781728124063
ISBN (Print)978-1-7281-2407-0
DOIs
Publication statusPublished - 2019
Event4th IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019 - Umea, Sweden
Duration: 16 Jun 201920 Jun 2019

Conference

Conference4th IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019
CountrySweden
CityUmea
Period16/06/1920/06/19

    Research areas

  • Business critical workloads, CPU contention, Resource contention

ID: 56926017