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We present a mathematically rigorous Quality-of-Service (QoS) metric whichrelates the achievable quality of service metric (QoS) for a real-timeanalytics service to the server energy cost of offering the service. Using anew iso-QoS evaluation methodology, we scale server resources to meet QoStargets and directly rank the servers in terms of their energy-efficiency andby extension cost of ownership. Our metric and method are platform-independentand enable fair comparison of datacenter compute servers with significantarchitectural diversity, including micro-servers. We deploy our metric andmethodology to compare three servers running financial option pricing workloadson real-life market data. We find that server ranking is sensitive to datainputs and desired QoS level and that although scale-out micro-servers can beup to two times more energy-efficient than conventional heavyweight servers forthe same target QoS, they are still six times less energy efficient thanhigh-performance computational accelerators.

Iso-Quality of Service: Fairly Ranking Servers for Real-Time Data Analytics