Variable-sized map and locality-aware reduce on public-resource grids

Po Cheng Chen, Yen Liang Su, Jyh Biau Chang, Ce Kuen Shieh

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

5 Citations (Scopus)


This paper presents a grid-enabled MapReduce framework called "Ussop". Ussop provides its users with a set of C-language based MapReduce APIs and an efficient runtime system for exploiting the computing resources available on public-resource grids. Considering the volatility nature of the grid environment, Ussop introduces two novel task scheduling algorithms, namely: Variable-Sized Map Scheduling (VSMS) and Locality-Aware Reduce Scheduling (LARS). VSMS dynamically adjusts the size of the map tasks according to the computing power of grid nodes. Moreover, LARS minimizes the data transfer cost of exchanging the intermediate data over a wide-area network. The experimental results indicate that both VSMS and LARS achieved superior performance than the conventional scheduling algorithms.

Original languageEnglish
Title of host publicationAdvances in Grid and Pervasive Computing - 5th International Conference, GPC 2010, Proceedings
Number of pages10
Publication statusPublished - 2010 Jun 25
Event5th International Conference on Advances in Grid and Pervasive Computing, GPC 2010 - Hualien, Taiwan
Duration: 2010 May 102010 May 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6104 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other5th International Conference on Advances in Grid and Pervasive Computing, GPC 2010

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Variable-sized map and locality-aware reduce on public-resource grids'. Together they form a unique fingerprint.

Cite this