Abstract
As the core computing mode of cloud computing, MapReduce is limited by the non-global parallel computing model that it is not easy to perform iterative synchronization algorithm. So it is impossible to perform fine-grained task adjustment based on semantics. This paper breaks through the original computational model of MapReduce, ensures that the existing computational model is compatible with the old MapReduce jobs, and introduces the heartbeat synchronization mechanism that allows the changed state data to interact between Parallel Layers of parallel tasks. This system provides a highly flexible message custom interface, and an adaptive message passing mechanism is designed for different application requirements, which supports algorithms with iterative processing and requirements of data interaction more efficiently. The experimental results on the real large-scale graph dataset showed that compared to the original MapReduce job external processing, the internal Parallel Layer iterative computation model proposed in this paper greatly reduces the processing time of the Mapreduce processing iterative algorithm.
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