English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

We propose constant approximation algorithms for generalizations of theFlexible Flow Shop (FFS) problem which form a realistic model fornon-preemptive scheduling in MapReduce systems. Our results concern theminimization of the total weighted completion time of a set of MapReduce jobson unrelated processors and improve substantially on the model proposed byMoseley et al. (SPAA 2011) in two directions. First, we consider each jobconsisting of multiple Map and Reduce tasks, as this is the key idea behindMapReduce computations, and we propose a constant approximation algorithm.Then, we introduce into our model the crucial cost of data shuffle phase, i.e.,the cost for the transmission of intermediate data from Map to Reduce tasks. Infact, we model this phase by an additional set of Shuffle tasks for each joband we manage to keep the same approximation ratio when they are scheduled onthe same processors with the corresponding Reduce tasks and to provide also aconstant ratio when they are scheduled on different processors. This is themost general setting of the FFS problem (with a special third stage) for whicha constant approximation ratio is known.

Scheduling MapReduce Jobs and Data Shuffle on Unrelated Processors