Modular Model of a Data Centre as a Tool for Improving Its Energy Efficiency

Berezovskaya, Yulia; Yang, Chen Wei; Mousavi, Arash Kheyraddini; Vyatkin, Valeriy; Minde, T.B.

doi:10.1109/access.2020.2978065

Cited by 26 publications

(18 citation statements)

References 33 publications

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“…Modelling of the datacentre's racks, pumps and chillers consumption provided by Mousavi et al [10] and Berezovskaya et al [11]. A datacentre toolbox presented in the latter article focuses on the modularity of the model, which can be used to construct a model of a datacentre of any size.…”

Section: Related Workmentioning

confidence: 99%

On Modelling of Edge Datacentre Microgrid for Participation in Smart Energy Infrastructures

Galkin

Berezovskaya

Yang

et al. 2022

IEEE Open J. Ind. Electron. Soc.

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Section: Related Workmentioning

confidence: 99%

On Modelling of Edge Datacentre Microgrid for Participation in Smart Energy Infrastructures

Galkin

Berezovskaya

Yang

et al. 2022

IEEE Open J. Ind. Electron. Soc.

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“…The power consumption models of different components of the IT load section and the IPSS are presented in [18]- [20], however, the models are limited to identify the component level consumption and losses considering the dynamic structures and topologies of DCs. The modular modeling approaches can consider the dynamic structures of the load sections for power consumption modeling from component-level to aggregated-level [6], [21], [22]. The earliest processor utilization based power consumption model of CPU appeared in 2002 [23].…”

Section: B Power Consumption Modeling Of It Loads and Power Conditioning Devicesmentioning

confidence: 99%

A Novel Reliability Index to Assess the Computational Resource Adequacy in Data Centers

2021

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The energy demand of data centers is increasing globally with the increasing demand for computational resources to ensure the quality of services. It is important to quantify the required resources to comply with the computational workloads at the rack-level. In this paper, a novel reliability index called loss of workload probability is presented to quantify the rack-level computational resource adequacy. The index defines the right-sizing of the rack-level computational resources that comply with the computational workloads, and the desired reliability level of the data center investor. The outage probability of the power supply units and the workload duration curve of servers are analyzed to define the loss of workload probability. The workload duration curve of the rack, hence, the power consumption of the servers is modeled as a function of server workloads. The server workloads are taken from a publicly available data set published by Google. The power consumption models of the major components of the internal power supply system are also presented which shows the power loss of the power distribution unit is the highest compared to the other components in the internal power supply system. The proposed reliability index and the power loss analysis could be used for rack-level computational resources expansion planning and ensures energy-efficient operation of the data center.INDEX TERMS Adequacy, data center, energy losses, internal power supply system, reliability.

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“…Mathematical models and machine-leaning-based approaches are used to address such tradeoffs [ 4 , 29 , 34 ] with varying levels of success. Mathematical models of the temperature evolution in a server room are presented in [ 35 , 36 ] addressing the thermal behavior concerning heat generation, circulation, and air-cooling system using Navier–Stokes equations expressing thermal laws or by using fast approximate solvers [ 37 , 38 ].…”

Section: Related Workmentioning

confidence: 99%

Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers

Antal

Cristea²,

Pădurean

et al. 2021

Sensors

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Data centers consume lots of energy to execute their computational workload and generate heat that is mostly wasted. In this paper, we address this problem by considering heat reuse in the case of a distributed data center that features IT equipment (i.e., servers) installed in residential homes to be used as a primary source of heat. We propose a workload scheduling solution for distributed data centers based on a constraint satisfaction model to optimally allocate workload on servers to reach and maintain the desired home temperature setpoint by reusing residual heat. We have defined two models to correlate the heat demand with the amount of workload to be executed by the servers: a mathematical model derived from thermodynamic laws calibrated with monitored data and a machine learning model able to predict the amount of workload to be executed by a server to reach a desired ambient temperature setpoint. The proposed solution was validated using the monitored data of an operational distributed data center. The server heat and power demand mathematical model achieve a correlation accuracy of 11.98% while in the case of machine learning models, the best correlation accuracy of 4.74% is obtained for a Gradient Boosting Regressor algorithm. Also, our solution manages to distribute the workload so that the temperature setpoint is met in a reasonable time, while the server power demand is accurately following the heat demand.

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Modular Model of a Data Centre as a Tool for Improving Its Energy Efficiency

Cited by 26 publications

References 33 publications

On Modelling of Edge Datacentre Microgrid for Participation in Smart Energy Infrastructures

On Modelling of Edge Datacentre Microgrid for Participation in Smart Energy Infrastructures

A Novel Reliability Index to Assess the Computational Resource Adequacy in Data Centers

Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers

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