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This paper studies the computation-communication tradeoff in a heterogeneousMapReduce computing system where each distributed node is equipped withdifferent computation capability. We first obtain an achievable communicationload for any given computation load and any given function assignment at eachnode. The proposed file allocation strategy has two steps: first, the inputfiles are partitioned into disjoint batches, each with possibly different sizeand computed by a distinct node; then, each node computes additional files fromits non-computed files according to its redundant computation capability. Inthe Shuffle phase, coded multicasting opportunities are exploited thanks to therepetitive file allocation among different nodes. Based on this scheme, wefurther propose the computation-aware and the shuffle-aware functionassignments. We prove that, by using proper function assignments, ourachievable communication load for any given computation load is within aconstant multiplicative gap to the optimum in an equivalent homogeneous systemwith the same average computation load. Numerical results show that our schemewith shuffle-aware function assignment achieves bettercomputation-communication tradeoff than existing works in some cases.

Heterogeneous Coded Distributed Computing: Joint Design of File Allocation and Function Assignment