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Although system heterogeneity has been extensively studied in the past, thereis yet to be a study on measuring the impact of heterogeneity on systemperformance. For this purpose, we propose a heterogeneity measure that cancharacterize the impact of the heterogeneity of a system on its performancebehavior in terms of throughput or makespan. We develop a mathematical model tocharacterize a heterogeneous system in terms of its task and machineheterogeneity dimensions and then reduce it to a single value, calledHomogeneous Equivalent Execution Time (HEET), which represents the executiontime behavior of the entire system. We used AWS EC2 instances to implement areal-world machine learning inference system. Performance evaluation of theHEET score across different heterogeneous system configurations demonstratesthat HEET can accurately characterize the performance behavior of thesesystems. In particular, the results show that our proposed method is capable ofpredicting the true makespan of heterogeneous systems without onlineevaluations with an average precision of 84%. This heterogeneity measure isinstrumental for solution architects to configure their systems proactively tobe sufficiently heterogeneous to meet their desired performance objectives.

HEET: A Heterogeneity Measure to Quantify the Difference across Distributed Computing Systems