Improving energy efficiency for transactional workloads in cloud environments

Thi Thao Nguyen Ho, Marco Gribaudo, Barbara Pernici

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

7 Citations (Scopus)

Abstract

Research on energy efficiency in data centers has been focusing on reducing energy consumption, and state-of-the-art techniques have been emphasizing on optimizing power and energy consumption at hardware and infrastructure levels of data centers. Although these techniques have achieved significant improvement in reducing the energy consumption of data centers, the increasing heterogeneity of the current workloads call for more holistic approaches to enable optimization at higher levels. the goal of this work is to look for new opportunities to further improve energy efficiency at the level of applications with a focus on transactional workloads. In particular, we propose the model to characterize the energy per job of transactional-based applications. the model is experimentally validated on a real federated cloud infrastructure. Alternative policies to optimize the energy consumption of transactional applications are evaluated on the basis of the model.

Original languageEnglish
Title of host publicatione-Energy 2017 - Proceedings of the 8th International Conference on Future Energy Systems
Number of pages6
PublisherAssociation for Computing Machinery (ACM)
Publication date16 May 2017
Pages290-295
ISBN (Electronic)9781450350365
DOIs
Publication statusPublished - 16 May 2017
Externally publishedYes
Event8th ACM International Conference on Future Energy Systems, e-Energy 2017 - Shatin, Hong Kong
Duration: 16 May 201719 May 2017

Conference

Conference8th ACM International Conference on Future Energy Systems, e-Energy 2017
Country/TerritoryHong Kong
CityShatin
Period16/05/201719/05/2017
SponsorACM SigComm
Seriese-Energy 2017 - Proceedings of the 8th International Conference on Future Energy Systems

Bibliographical note

Publisher Copyright:
© 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Keywords

  • Data center
  • Energy efficiency
  • Transactional workloads

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