Achieving Elasticity for Cloud MapReduce Jobs

Khaled Salah, Jose Alcaraz Calero

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

9 Citations (Scopus)

Abstract

These days, both the cloud computing paradigm and MapReduce programming framework have become key enablers for running big data analytics and large-scale compute-and data-intensive applications. Achieving proper elasticity for cloud MapReduce jobs is a critical research problem that has been overlooked. In this paper, we focus on how to achieve proper elasticity for MapReduce jobs when executed on cloud clusters. In particular, we present an analytical queueing model that can be used to determine at any given time and under different workload conditions the minimal number of mappers and reducers needed to satisfy the Service Level Objective (SLO) response time.
Original languageEnglish
Title of host publicationPROCEEDINGS OF THE 2013 IEEE 2ND INTERNATIONAL CONFERENCE ON CLOUD NETWORKING (CLOUDNET)
EditorsX Fu, P Sharma, D Huang, D Medhi
PublisherIEEE
Pages195-199
ISBN (Print)978-1-4799-0568-3
DOIs
Publication statusPublished - 2013

Publication series

NameIEEE International Conference on Cloud Networking
PublisherIEEE
ISSN (Print)2374-3239

Keywords

  • MapReduce
  • Cloud Computing
  • Elasticity
  • Netwrok and Sevice Delays
  • Queueing Analysis
  • Performance

Cite this

Salah, K., & Alcaraz Calero, J. (2013). Achieving Elasticity for Cloud MapReduce Jobs. In X. Fu, P. Sharma, D. Huang, & D. Medhi (Eds.), PROCEEDINGS OF THE 2013 IEEE 2ND INTERNATIONAL CONFERENCE ON CLOUD NETWORKING (CLOUDNET) (pp. 195-199). (IEEE International Conference on Cloud Networking). IEEE. https://doi.org/10.1109/CloudNet.2013.6710577