EEUI: a new measure to monitor and manage energy efficiency in data centers

Abstract

Purpose Data centers (DCs) are similar to traditional factories in many aspects like response time constraints, limited capacity, and utilization levels. Several indicators have been developed to monitor and compare productivity in manufacturing. However, in DCs most used indicators focus on technical aspects of infrastructure, not efficiency of operations. The purpose of this paper is to rely on operations management to define a commensurate and proportionate DC performance indicator: the energy-efficient utilization indicator (EEUI). EEUI makes objective and comparative assessment of efficiency possible independently of the operating environment and its constraints. Design/methodology/approach The authors followed a design science approach, which follows the practitioner’s initial steps for finding solutions to business relevant problems prior to theory building. Therefore, this approach fits well with this research, as it is primarily motivated by business and management needs. EEUI combines both the amount of energy consumed by different components and their current energy efficiency (EE). It reaches its highest value when all server components are optimally loaded in EE sense. The authors tested EEUI by collecting data from three scientific DCs and performing controlled laboratory tests. Findings The results indicate that the optimization of EEUI makes it possible to run computing resources more efficiently. This leads to a higher EE and throughput of the DC while reducing the carbon footprint associated to DC operations. Both energy-related costs and the total cost of ownership are consequently reduced, since the amount of both energy and hardware resources needed decrease, while improving DC sustainability. Practical implications In comparison with current DC operations, the results imply that using the EEUI could help increase the EE of DCs. In order to optimize the proposed EEUIs, DC managers and operators should use resource management policies that increase the resource usage variation of the jobs being processed in the same computing resources (e.g. servers). Originality/value The paper provides a novel approach to monitor the EE at which computing resources are used. The proposed indicator not only considers the utilization levels at which server components are used but also takes into account their EE and energy proportionality.

The emergence of data centres as an innovative alternative property sector

“…Despite being considered as a nascent asset investment class, the increasing significance of the alternative property sectors has attracted a number of empirical investigations on their risk-adjusted performance and portfolio enhancement attributes, both in the context of broad alternative property sectors (McIntosh et al , 2017) and sector-specific coverage such as healthcare properties (Newell and Marzuki, 2018a) and student accommodation (Newell and Marzuki, 2018b). Meanwhile, previous studies on data centre properties are confined to issues pertaining to valuation applications (McAllister and Loizou, 2009), planning considerations (Jones, Hillier and Comfort, 2013; Jones, Hillier, Comfort and Clarke-Hill, 2013), sustainability practices (Jones, Hillier and Comfort, 2013; Jones, Hillier, Comfort and Clarke-Hill, 2013; Shuja et al , 2016) and energy consumption and efficiency (Abaunza et al , 2018; Depoorter et al , 2015; Flucker et al , 2018; Oro et al , 2015). To the best of the authors’ knowledge, no previous study has explored the institutional investment opportunity in data centres.…”

Towards Green Big Data at CERN

“…For most of the components the highest energy efficiency is reached when utilization is high but not too close to 100%. For this purpose we [81] have developed the energy-efficient utilization indicator (EEUI). This indicator gives a weighted average of energy efficiency for all components of a computing unit (single server to data center).…”