A fast map reduce algorithm for exact-repair reconstruction of big-data in cloud storage

In the distributed cloud storage for big data systems, there is a need for exact repair, high bandwidth codes. Instead of simply replicating the entire data, exact repair only focus on the error ones. The challenge for exact repair in big-data storage is to simultaneously enable the very high bandwidth repair using Map-Reduce, Simple Regenerating Code schemes and to combine with maximally distance separable (MDS) exact repair for the rare, but exceptional outlier error patterns requiring optimum erasure code reconstruction.;In this thesis, we apply an optimum fast bandwidth repair algorithm for a big-data source. We build a cloud system framework to place this big-data source. And through the specific allocation we are able to use exact repair reconstruction (simple regeneration code). We also propose an innovation to the Map-Reduce so that we can apply our reconstruction in parallel.;With the tremendously fast copy speed in Hadoop system and up to 2/3 code rate for SRC, in both GF(2) and GF(q) field. This cloud system will show up a better performance.