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

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 heavyweight servers for the same target QoS, they are still six times less energy efficient than high-performance computational accelerators.