Towards performance modeling as a service by exploiting resource diversity in the public cloud

Mark Meredith, Bhuvan Urgaonkar

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

1 Scopus citations

Abstract

Cloud computing platforms such as Amazon EC2, Google Computing Engine, and Microsoft Azure offer dozens of virtual machine (VM) types with a wide range of resource capacity vs. price trade-offs, requiring a customer to consider numerous resource configurations when evaluating service needs. This report investigates the possibility of using the diversity of VM types to predict the performance of new VM types using black box modeling. The performance model used is a multiple linear regression of the average server response time, server load (throughput in requests per second), the number of CPU cores, and the memory in the procured VM. For three commonly used database servers-Redis (key-value stores), Apache Cassandra (NoSQL) and MySQL-the model accuracy increases for larger sets of VMs. E.g., for Redis, the measure of model efficacy improves from 0.4-0.5 with 2 VM types for training and 0.7 for 3 VM types to 0.8 for 4 VM types. These results suggest further interesting research challenges, such as the possibility of automating the process of calibrating performance models using diverse resource types on a public cloud leading to "performance modeling as a service."

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE 9th International Conference on Cloud Computing, CLOUD 2016
EditorsIan Foster, Nimish Radia, Ian Foster
PublisherIEEE Computer Society
Pages204-211
Number of pages8
ISBN (Electronic)9781509026197
DOIs
StatePublished - Jan 17 2017
Event9th International Conference on Cloud Computing, CLOUD 2016 - San Francisco, United States
Duration: Jun 27 2016Jul 2 2016

Publication series

NameIEEE International Conference on Cloud Computing, CLOUD
ISSN (Print)2159-6182
ISSN (Electronic)2159-6190

Other

Other9th International Conference on Cloud Computing, CLOUD 2016
CountryUnited States
CitySan Francisco
Period6/27/167/2/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems
  • Software

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