Iso-Quality of Service: Fairly Ranking Servers for Real-Time Data Analytics.
PARALLEL PROCESSING LETTERS(2015)
摘要
We present a mathematically rigorous iso-Quality-of-Service (QoS) metric which relates the achievable quality of service (QoS) for a real-time analytics service with workload specific and use case specific performance and output quality requirements to the energy cost of offering the service by different server architectures. 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.
更多查看译文
关键词
Micro-servers,datacentres,real-time analytics,computational finance,performance analysis,energy optimisation,event processing,quality of service
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络