Packing Algorithms for Big Data Replay on Multicore

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter discusses optimization in a new environment created as an alternative to Hadoop/MapReduce. The core idea is to bring the bulk from now-passive shard nodes to a dedicated machine and replay it locally while a large number of jobs are running on multicore. This chapter discusses optimization methods for machines with a large number of cores and processing jobs. This chapter also discusses how the new architecture can easily accommodate advanced Big Data-related statistics, namely streaming algorithms.

Original languageEnglish
Title of host publicationBig Data
Subtitle of host publicationPrinciples and Paradigms
PublisherElsevier Inc.
Pages239-266
Number of pages28
ISBN (Electronic)9780128093467
ISBN (Print)9780128053942
DOIs
Publication statusPublished - 3 Jun 2016

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Keywords

  • Big Data replay method
  • Data streaming
  • Hadoop
  • MapReduce
  • Massively multicore
  • Packing algorithms

Cite this

Marat, Z. (2016). Packing Algorithms for Big Data Replay on Multicore. In Big Data: Principles and Paradigms (pp. 239-266). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-805394-2.00010-6