Iso-Quality of Service: Fairly Ranking Servers for Real-Time Data Analytics
Giorgis Georgakoudis, Charles J. Gillan, Ahmed Sayed, Ivor Spence,, Richard Faloon, and Dimitrios S. Nikolopoulos

TL;DR
This paper introduces a rigorous QoS metric linking service quality to energy costs, enabling fair server comparisons across diverse architectures for real-time analytics.
Contribution
It presents a platform-independent iso-QoS evaluation methodology for ranking servers based on energy efficiency at specific QoS levels.
Findings
Micro-servers can be twice as energy-efficient as traditional servers for the same QoS.
Server ranking varies with data inputs and QoS targets.
High-performance accelerators are six times more energy-efficient than micro-servers.
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…
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Taxonomy
TopicsCloud Computing and Resource Management · Parallel Computing and Optimization Techniques · Advanced Data Storage Technologies
