Title :
Local and Global Optimization of MapReduce Program Model
Author :
Liu, Congchong ; Zhou, Shujia
Author_Institution :
Univ. of Maryland, Baltimore, MD, USA
Abstract :
MapReduce, which was introduced by Google, provides two functional interfaces, Map and Reduce, for a user to write the user-specific code to process the large amount of data. It has been widely deployed in cloud computing systems. The parallel tasks, data partition, and data transit are automatically managed by its runtime system. This paper proposes a solution to optimize the MapReduce program model and demonstrate it with X10. We develop an adaptive load distribution scheme to balance the load on each node and consequently reduce across-node communication cost occurring in the Reduce function. In addition, we exploit shared-memory in each node to further reduce the communication cost with multi-core programming.
Keywords :
cloud computing; optimisation; parallel programming; resource allocation; shared memory systems; Google; MapReduce program model; X10; adaptive load distribution scheme; cloud computing system; data partition; data transit; global optimization; multicore programming; parallel task; shared memory system; user specific code; Adaptation models; Computational modeling; Computers; Distributed databases; Load modeling; Monitoring; Runtime; MapReduce; X10 parallel programming language; distributed memory system; load balancing; shared-memory system;
Conference_Titel :
Services (SERVICES), 2011 IEEE World Congress on
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4577-0879-4
Electronic_ISBN :
978-0-7695-4461-8
DOI :
10.1109/SERVICES.2011.64
