Dynamic Data Partitioning and Virtual Machine Mapping: Efficient Data Intensive Computation

Kenn Slagter, Ching-Hsien Hsu, Yeh-Ching Chung
2013 2013 IEEE 5th International Conference on Cloud Computing Technology and Science  
Big data refers to data that is so large that it exceeds the processing capabilities of traditional systems. Big data can be awkward to work and the storage, processing and analysis of big data can be problematic. MapReduce is a recent programming model that can handle big data. MapReduce achieves this by distributing the storage and processing of data amongst a large number of computers (nodes). However, this means the time required to process a MapReduce job is dependent on whichever node is
more » ... ast to complete a task. This problem is exacerbated by heterogeneous environments. In this paper we propose a method to improve MapReduce execution in heterogeneous environments. This is done by dynamically partitioning data during the Map phase and by using virtual machine mapping in the Reduce phase in order to maximize resource utilization.
doi:10.1109/cloudcom.2013.134 dblp:conf/cloudcom/SlagterHC13 fatcat:54ool2qmcndazaxqqlimwib54e