A demonstration of monitoring and measuring data centers for energy efficiency using opensource tools

Gústafsson, Jónas; Fredriksson, Sebastian; Nilsson-Mäki, Magnus; Olsson, Daniel; Sarkinen, Jeffrey; Niska, Henrik; Seyvet, Nicolas; Minde, T.B.; Summers, J. L.

doi:10.1145/3208903.3213522

Cited by 12 publications

(10 citation statements)

References 4 publications

(6 reference statements)

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“…The SICS ICE is a data centre envisioned for different research projects and established by SICS Swedish ICT in partnership with Luleå University of Technology and it is located in Luleå, Sweden [26]. In this study, we focus on the simulation of Module 1 of the SICS ICE facility [40] which is demonstrated in FIGURE 1.…”

Section: A Modelling the Testbedmentioning

confidence: 99%

“…As shown in FIGURE 1 (a), the module is a 5 m×6.5 m room, consists of 10 server racks (Rack 1-10), and 4 cooling devices (SEE Cooler 1-4). The racks assemble the computational capabilities of Module 1, each rack contains 18 servers each of them equipped with four 4TB 7200rpm hard drives, 256GB RAM, two Intel Xeon E5-2620 v3 2.4GHz CPUs and six Delta PFR 60 × 60 local fans [40]. In terms of modelling of thermal behaviour and energy consumption, we use only models of CPUs and local fans as components of server model.…”

Section: A Modelling the Testbedmentioning

confidence: 99%

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

et al. 2020

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Section: A Modelling the Testbedmentioning

confidence: 99%

Section: A Modelling the Testbedmentioning

confidence: 99%

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

et al. 2020

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“…Traditionally, the data center industry has addressed inefficiencies primarily through hardware innovation -more efficient CPUs, better cooling solutions, improved power distribution systems, etc. However, there is huge scope to offer better efficiencies through software-only solutionsexamples include more powerful monitoring and analysis of data center systems [16], predictive capacity planning [17], CFD-based floor layout tools [18], and intelligent scheduling mechanisms [19]. One common factor that is necessary when developing real-world solutions in this space is the need for multi-disciplinary, holistic thinking -it is not enough to view such a system in isolation; instead solutions need to consider the interactions between the physical systems, software systems, business processes, and user behaviors.…”

Section: Takedownmentioning

confidence: 99%

Invited Paper: Improving Data Center Efficiency Through Holistic Scheduling In Kubernetes

Townend¹,

Clement²,

Burdett³

et al. 2019

2019 IEEE International Conference on Service-Oriented System Engineering (SOSE)

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Data centers are the infrastructure that underpins modern distributed service-oriented systems. They are complex systems-of-systems, with many interacting elements, that consume vast amounts of power. Demand for such facilities is growing rapidly, leading to significant global environmental impact. The data center industry has conducted much research into efficiency improvements, but this has mostly been at the physical infrastructure level. Research into software-based solutions for improving efficiency is greatly needed. However, most current research does not take a holistic view of the data center that considers virtual and physical infrastructures as well as business process. This is crucial if a solution is to be applied in a realistic setting. This paper describes the complex, system-of-systems nature of data centers, and discusses the service models used in the industry. We describe a holistic scheduling system that replaces the default scheduler in the Kubernetes container system, taking into account both software and hardware models. We discuss the initial results of deploying this scheme in a real data center, where power consumption reductions of 10-20% were observed. We show that by introducing hardware modelling into a software-based solution, an intelligent scheduler can make significant improvements in data center efficiency. We conclude by looking at some of the future work that needs to be performed in this area.

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“…Detailed flow analysis of hard floor and raised floor setups in the SICS ICE data center in Luleå, Sweden, has previously been performed with numerical simulations [7,15,16] and measurements [17,18]. The influence of a turbulence model on the fluid flow in a hard floor server room was investigated by Wibron et al [15], recommending the Reynolds stress model (RSM) for modeling the flow on a server room scale.…”

Section: Introductionmentioning

confidence: 99%

Comparing Performance Metrics of Partial Aisle Containments in Hard Floor and Raised Floor Data Centers Using CFD

2019

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In data centers, efficient cooling systems are required to both keep the energy consumption as low as possible and to fulfill the temperature requirements. The aim of this work is to numerically investigate the effects of using partial aisle containment between the server racks for hard and raised floor configurations. The computational fluid dynamics (CFD) software ANSYS CFX was used together with the Reynolds stress turbulence model to perform the simulations. Velocity measurements in a server room were used for validation. Boundary conditions and the load of each rack were also retrieved from the experimental facility, implying an uneven load between the racks. A combination of the performance metrics Rack Cooling Index (RCI), Return Temperature Index (RTI) and Capture Index (CI) were used to evaluate the performance of the cooling systems for two supply flow rates at a 100% and 50% of operating condition. Based on the combination of performance metrics, the airflow management was improved in the raised floor configurations. With the supply flow rate set to operating conditions, the RCI was 100% for both raised floor and hard floor setups. The top- or side-cover fully prevented recirculation for the raised floor configuration, while it reduced the recirculation for the hard floor configuration. However, the RTI was low, close to 40% in the hard floor case, indicating poor energy efficiency. With the supply flow rate decreasing with 50%, the RTI increased to above 80%. Recirculation of hot air was indicated for all the containments when the supply rate was 50%, but the values of RCI still indicated an acceptable performance of the cooling system.

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A demonstration of monitoring and measuring data centers for energy efficiency using opensource tools

Cited by 12 publications

References 4 publications

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

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

Invited Paper: Improving Data Center Efficiency Through Holistic Scheduling In Kubernetes

Comparing Performance Metrics of Partial Aisle Containments in Hard Floor and Raised Floor Data Centers Using CFD

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