TY - JOUR
T1 - Iso-Quality of Service: Fairly Ranking Servers for Real-Time Data Analytics
AU - Georgakoudis, Giorgis
AU - Gillan, Charles
AU - Sayed, Ahmed
AU - Spence, Ivor
AU - Faloon, Richard
AU - Nikolopoulos, Dimitrios S.
PY - 2015/9
Y1 - 2015/9
N2 - 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.
AB - 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.
U2 - 10.1142/S0129626415410042
DO - 10.1142/S0129626415410042
M3 - Article
SN - 0129-6264
VL - 25
JO - Parallel Processing Letters
JF - Parallel Processing Letters
IS - 3
M1 - 1541004
ER -