SCIENCE@home

Optimising Cloud-Based Hadoop 2.x Applications

Naif Alasmari

Published 2018 in 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion)

ABSTRACT

Hadoop is an open-source MapReduce implementation widely adopted in industry and academia. However, achieving effective trade-offs between the cost and execution time of Hadoop applications is challenging due to the numerous Hadoop parameters that need to be configured properly. Running Hadoop applications on public clouds compounds this challenge because cloud parameters such as the number and types of virtual machines used to run the application also need to be decided. We will develop an approach for the multi-objective optimisation of Hadoop applications running on public clouds, enabling users to make the best of Hadoop processing when deployed in a cloud environment. The approach will provide those responsible for the configuration of a Hadoop application with a set of Pareto-optimal configurations, allowing them to run the application optimally within their time and/or budgetary constraints.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion)

  • Publication date

    2018-12-01

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1109/UCC-Companion.2018.00032
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-35 of 35 references · Page 1 of 1

CITED BY