Distributed computing has become an essential mode of big data computing. By contrast, single-user computing is difficult to overcome the high computational complexity of big data orders. On the other side, distributed computing will also increase the communication overhead of parallel computing resources due to data interactivity, thus decreasing the efficiency of big data computing. We are engaged in a novel strategy of balancing computing resources and efficiency, thus cutting overhead costs, including the computing and communication time, producing from distributed computing.

We are engaged in building models of parallel scalability with different complexity level for various computing problems in order to determine and identify parallel scalability problems of the same complexity. We also study design methods of parallel scalable algorithms for different types of computing problems. We are devoting to offering an integrated approach to balance relationships between multiple factors when computational performance gets improved.