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

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

Research output: Contribution to journalArticlepeer-review

14 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 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
Pages (from-to)843-849
Number of pages7
JournalFuture Generation Computer Systems
Issue number6
Publication statusPublished - 2011 Jun

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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