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

Giorgis Georgakoudis, Charles Gillan, Ahmed Sayed, Ivor Spence, Richard Faloon, Dimitrios S. Nikolopoulos

Research output: Contribution to journalArticle

2 Citations (Scopus)
200 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article number1541004
Number of pages17
JournalParallel Processing Letters
Volume25
Issue number3
DOIs
Publication statusPublished - Sep 2015

Fingerprint Dive into the research topics of 'Iso-Quality of Service: Fairly Ranking Servers for Real-Time Data Analytics'. Together they form a unique fingerprint.

  • Cite this