  Big data analytics on geographically distributed datasets (across data
centers or clusters) has been attracting increasing interests from both
academia and industry, but also significantly complicates the system and
algorithm designs. In this article, we systematically investigate the
geo-distributed big-data analytics framework by analyzing the fine-grained
paradigm and the key design principles. We present a dynamic global manager
selection algorithm (GMSA) to minimize energy consumption cost by fully
exploiting the system diversities in geography and variation over time. The
algorithm makes real-time decisions based on the measurable system parameters
through stochastic optimization methods, while achieving the performance
balances between energy cost and latency. Extensive trace-driven simulations
verify the effectiveness and efficiency of the proposed algorithm. We also
highlight several potential research directions that remain open and require
future elaborations in analyzing geo-distributed big data.
