  Coding theoretic approached have been developed to significantly reduce the
communication load in modern distributed computing system. In particular, coded
distributed computing (CDC) introduced by Li et al. can efficiently trade
computation resources to reduce the communication load in MapReduce like
computing systems. For the more general cascaded CDC, Map computations are
repeated at r nodes to significantly reduce the communication load among nodes
tasked with computing Q Reduce functions s times. In this paper, we propose a
novel low-complexity combinatorial design for cascaded CDC which 1) determines
both input file and output function assignments, 2) requires significantly less
number of input files and output functions, and 3) operates on heterogeneous
networks where nodes have varying storage and computing capabilities. We
provide an analytical characterization of the computation-communication
tradeoff, from which we show the proposed scheme can outperform the
state-of-the-art scheme proposed by Li et al. for the homogeneous networks.
Further, when the network is heterogeneous, we show that the performance of the
proposed scheme can be better than its homogeneous counterpart. In addition,
the proposed scheme is optimal within a constant factor of the information
theoretic converse bound while fixing the input file and the output function
assignments.
