Research On MapReduce Parallel Programming Model In The Cloud Computing

Cloud computing is parallel computing, distributed computing and grid computing's development, and make parallel technology into people's life. Cloud computing, technology of personal high-performance computer (PHPC) developed deeply, which make many technical personnel to start working from Stand-alone mode transfer to parallel computing mode. The popular of Cloud computing make parallel programming as a key problem many programmers must confront and resolve.Google suggest the MapReduce parallel programming model greatly reduced difficulty of the parallel programming. Comparing with traditional distributed program design, MapReduce encapsulates the parallel processing, tolerant, localization calculation, load balancing etc. details. Also provides a simple and powerful programming interface, and greatly simplifies the design of parallel programs.This paper firstly introduces the concept of cloud computing, basic theory and research status, and state several traditional parallel programming models, analyses and studies its principle and development. Briefly introduce Google computing clouds and Hadoop cloud computing structure, and compare MapReduce will with the MPI, studies the difference between the two with their respective advantages.This paper elaborates the thoughts of MapReduce programming in details, analyzes and studies principle of MapReduce solving work problems and specific steps and methods. MapReduce fault is introduced, and scheduling algorithm of MapReduce is analyzed in details when in working. then studies the difference for properties of MapReduce in heterogeneous Hadoop cluster environment, and analysis the influence on MapReduce in heterogeneous environment. This article suggests a new data distribution mechanism HDDM, according to calculation ratio of heterogeneous cluster nodes input file, improve performance of MapReduce in heterogeneous Hadoop cluster.Finally, the experiments show that the proposed data allocation mechanism HDDM can greatly improve the efficiency of MapReduce programs.