  We present a mathematically rigorous Quality-of-Service (QoS) metric which
relates the achievable quality of service metric (QoS) for a real-time
analytics service to the server energy cost of offering the service. Using a
new iso-QoS evaluation methodology, we scale server resources to meet QoS
targets and directly rank the servers in terms of their energy-efficiency and
by extension cost of ownership. Our metric and method are platform-independent
and enable fair comparison of datacenter compute servers with significant
architectural diversity, including micro-servers. We deploy our metric and
methodology to compare three servers running financial option pricing workloads
on real-life market data. We find that server ranking is sensitive to data
inputs and desired QoS level and that although scale-out micro-servers can be
up to two times more energy-efficient than conventional heavyweight servers for
the same target QoS, they are still six times less energy efficient than
high-performance computational accelerators.
