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

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.

Expediting assessments of database performance for streams of respiratory parameters

“…We find that the re-sampling technique generally gives answers within a few percent of those obtained by very many repetitions of the experiments. This is one of the key contributions of our paper, extending beyond the work that we have previously reported for financial options [1,2].…”

“…Our previous work presented three metrics: Seconds per Option (S/ Opt), Joules per Option (J/Opt) and Quality of Service (QoS) [1,2]. Replacing an execution of an option pricing kernel with the execution of an SQL INSERT, these metrics can be extended directly to milliseconds per Insert (MS.INS) and Joules per Insert (J.INS) with the QoS similarly redefined in terms of successful insert operations.…”

“…In previous work in the FP7 NanoStreams project [1,2] we studied the performance and energy efficiency of servers consuming a financial data feed and computing option contract prices in real-time from the stock prices contained in the data feed. In this present paper we apply and extend the methodology to the case of streams of patient respiratory data.…”

NanoStreams: Codesigned microservers for edge analytics in real time

“…• Workload-specific optimisation using the concept of isoquality of service (iso-QoS), applied to three use cases from the healthcare, capital markets, and business analytics sectors (Section V) [6], [7]. • A range of new methods to fairly compare the efficiency of server architectures (Section VI) and scale these architectures on demand to meet workload QoS requirements [6], [7].…”

“…• A range of new methods to fairly compare the efficiency of server architectures (Section VI) and scale these architectures on demand to meet workload QoS requirements [6], [7]. NanoStreams advances the state of the art in micro-servers in several ways by: (a) adding application-specific but programmable hardware accelerators to micro-servers, as opposed to existing solutions that use elaborate hardware design flows and target a single algorithm [8]; (b) providing general purpose low latency networking to access accelerators in the datacentre, as opposed to custom fabrics [9]; (c) effectively integrating streaming and accelerator-aware programming models into domain specific software stacks, moving one step ahead of ongoing efforts to unify heterogeneous programming models [10]; (d) significantly improving server energy-efficiency of micro-servers via on demand and QoS-aware scale-out and acceleration [6], [7].…”