Heterogeneous Architectures for Big Data Batch Processing in MapReduce Paradigm

The amount of digital data produced worldwide is exponentially growing. While the source of this data, collectively known as Big Data, varies from among mobile services to cyber physical systems and beyond, the invariant is their increasingly rapid growth for the foreseeable future. Immense incentives exist, from marketing campaigns to forensics and to research in social sciences, that motivate processing increasingly bigger data so as to extract information and knowledge for the betterment of processes and benefits. Consequently, the need for more efficient computing systems tailored to such big data applications is increasingly intensified. Such custom architectures would expectedly embrace heterogeneity to better match each phase of the computation. In this paper we review state of the art as well as envisioned future large-scale computing architectures customized for batch processing of big data applications in the MapReduce paradigm. We also provide our view of current important trends relevant to such systems, and their impacts on future architectures and architectural features expected to address the needs of tomorrow big data processing in this paradigm.