2015/08/05 by Foto Afrati, Shlomi Dolev, Afrati, Foto +5
Computer Science · #Cloud Computing and Resource Management #Cryptography and Data Security #Databases (cs.DB) #Distributed #FOS: Computer and information sciences #Graph Theory and Algorithms #Parallel #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1508.01171
openalex publication_date 2015/08/05 · openalex created_date 2022/09/29 · openalex updated_date 2026/07/28
MapReduce has proven to be one of the most useful paradigms in the revolution\nof distributed computing, where cloud services and cluster computing become the\nstandard venue for computing. The federation of cloud and big data activities\nis the next challenge where MapReduce should be modified to avoid (big) data\nmigration across remote (cloud) sites. This is exactly our scope of research,\nwhere only the very essential data for obtaining the result is transmitted,\nreducing communication, processing and preserving data privacy as much as\npossible. In this work, we propose an algorithmic technique for MapReduce\nalgorithms, called Meta-MapReduce, that decreases the communication cost by\nallowing us to process and move metadata to clouds and from the map phase to\nreduce phase. In Meta-MapReduce, the reduce phase fetches only the required\ndata at required iterations, which in turn, assists in preserving the data\nprivacy.\n