Data Center Equipment Research Articles

Modeling noise from large-scale data center equipment

Data centers present unique noise modeling challenges due to the scale of equipment needed to meet cooling and backup power demands. Three-dimensional environmental noise modeling software that is based on ISO 9613-2 "Attenuation of sound during propagation outdoors" offers a range of modeling techniques. While more detailed modeling techniques may offer more accuracy in the near field, they also increase model calculation run times. Additionally, manufacturer sound emissions data often more closely align with simplified modeling techniques. This evaluation reviews common data center noise sources, reviews the range of manufacturer noise emissions data typically available for such sources, and compares noise modeling techniques with reference to on-site observations.

Integrating project management with advanced cooling solutions for data centers: A path to enhanced energy efficiency

Data centers play a crucial role in modern digital infrastructure, but their energy consumption and associated carbon footprint are significant concerns. Among the various energy-consuming components of data centers, cooling systems stand out as major contributors. This paper explores the integration of project management principles with advanced cooling solutions to enhance energy efficiency in data centers. By effectively managing projects and implementing innovative cooling technologies, data center operators can achieve significant reductions in energy consumption and operational costs. The integration of project management practices with advanced cooling solutions involves several key steps. First, project management methodologies such as Agile or Waterfall are applied to plan and execute the implementation of advanced cooling technologies. This includes assessing current cooling systems, identifying suitable advanced solutions, and developing a timeline for implementation. Next, advanced cooling solutions such as liquid cooling, containment systems, or free cooling are deployed to reduce the energy required for cooling data center equipment. One of the primary benefits of integrating project management with advanced cooling solutions is enhanced energy efficiency. Advanced cooling technologies can significantly reduce the energy consumed by data center cooling systems, leading to lower operational costs and a smaller carbon footprint. Additionally, the use of project management methodologies ensures that the implementation of these technologies is carried out efficiently and effectively. Furthermore, this integration can lead to improved data center performance and reliability. By optimizing cooling systems, data center operators can ensure that IT equipment operates within optimal temperature ranges, reducing the risk of overheating and downtime. Additionally, the implementation of advanced cooling solutions can future-proof data centers against increasing heat loads from high-density computing equipment. In conclusion, integrating project management with advanced cooling solutions offers a promising path to enhance energy efficiency in data centers. By adopting this approach, data center operators can achieve significant energy savings, reduce their environmental impact, and improve overall operational efficiency.

A multi-setpoint cooling control approach for air-cooled data centers using the deep Q-network algorithm

Cooling systems provide a safe thermal environment for the reliable operation of IT equipment in data centers (DCs) while generating significant energy consumption. Therefore, to achieve energy savings in cooling system control under dynamic thermal distribution in DCs, this paper proposes a multi-setpoint cooling control approach based on deep reinforcement learning (DRL). Firstly, a thermal model based on the XGBoost algorithm is constructed to precisely evaluate the thermal distribution in the rack room to guide real-time cooling control. Secondly, a multi-set point cooling control approach based on the deep Q-network algorithm (DQN-MSP) is designed to finely regulate the supply air temperature of each air conditioner by capturing the thermal fluctuations to ensure the dynamic balance of cooling supply and demand. Finally, we adopt the extended CloudSimPy simulation tool and the real workload trace of the PlanetLab system to evaluate the effectiveness and performance of the proposed approach. The simulation results show that the proposed control solution effectively reduces the cooling energy consumption by over 2.4% by raising the average air supply temperature of the air conditioner while satisfying the thermal constraints.

Chasing Carbon: The Elusive Environmental Footprint of Computing

Given recent algorithm, software, and hardware innovation, computing has enabled a plethora of new applications. As computing becomes increasingly ubiquitous, however, so does its environmental impact. This article brings the issue to the attention of computer-systems researchers. Our analysis, built on industry-reported characterization, quantifies the environmental effects of computing in terms of carbon emissions. Broadly, carbon emissions have two sources: operational energy consumption, and hardware manufacturing and infrastructure. Although carbon emissions from the former are decreasing, thanks to algorithmic, software, and hardware innovations that boost performance and power efficiency, the overall carbon footprint of computer systems continues to grow. This work quantifies the carbon output of computer systems to show that most emissions related to modern mobile and data-center equipment come from hardware manufacturing and infrastructure. We, therefore, outline future directions for minimizing the environmental impact of computing systems.

Data characteristics aware prediction model for power consumption of data center servers

SummaryDue to the rapid increase in the number and scale of data centers, the information and communication technology (ICT) equipment in data centers consumes an enormous amount of power. A power prediction model is therefore essential for decision‐making optimization and power management of ICT equipment. However, it is difficult to predict the power consumption of data centers accurately due to the complex power patterns and nonlinear interdependencies among components. Existing methods either rely on standard formulas, or simply treat it as time series, both leading to poor power prediction accuracy. To overcome those limitations, in this article, we present a systematic power prediction framework called characteristic aware attention‐augmented deep learning‐based prediction method. In particular, we first analyze the different power consumption series to illustrate their different temporal characteristics. Second, we perform different data processing for the corresponding characteristics of power series samples. Third, we propose an accurate and efficient neural network model to predict future power consumption with the pretreated data. The experimental results show that the proposed model is able to achieve superior prediction accuracy.

Simplified and Detailed Analysis of Data Center Particulate Contamination at Server and Room Level Using Computational Fluid Dynamics

Abstract Continuous rise in cloud computing and other web-based services propelled the data center proliferation seen over the past decade. Traditional data centers use vapor-compression-based cooling units that not only reduce energy efficiency but also increase operational and initial investment costs due to involved redundancies. Free air cooling and airside economization can substantially reduce the information technology equipment (ITE) cooling power consumption, which accounts for approximately 40% of energy consumption for a typical air-cooled data center. However, this cooling approach entails an inherent risk of exposing the ITE to harmful ultrafine particulate contaminants, thus, potentially reducing the equipment and component reliability. The present investigation attempts to quantify the effects of particulate contamination inside the data center equipment and ITE room using computational fluid dynamics (CFD). An analysis of the boundary conditions to be used was done by detailed modeling of ITE and the data center white space. Both two-dimensional and three-dimensional simulations were done for detailed analysis of particle transport within the server enclosure. An analysis of the effect of the primary pressure loss obstructions like heat sinks and dual inline memory modules inside the server was done to visualize the localized particle concentrations within the server. A room-level simulation was then conducted to identify the most vulnerable locations of particle concentration within the data center space. The results show that parameters such as higher velocities, heat sink cutouts, and higher aspect ratio features within the server tend to increase the particle concentration inside the servers.

The significance of product design in the circular economy: A sustainable approach to the design of data centre equipment as demonstrated via the CEDaCI design case study

Society depends on fast, uninterrupted 24/7 data transfer in almost every aspect of our lives. Data centres (DC) across the globe process billions of gigabytes of data every day. There are currently about 7 million data centres globally and the sector is expected to grow fivefold by 2030, inevitably increasing already high demand for millions of tonnes of resources, including critical raw materials (CRM). The infrastructure for recycling sectoral waste is lacking, wasting millions of tonnes of materials. Many virgin materials are located in conflict zones, which jeopardises the supply chain that the sector heavily relies on, consequently posing a danger of severe material shortages in the future. Present design thinking does not balance optimisation of the performance and operational energy requirements and designing for circularity. To date, server design has mainly been focused on energy efficiency. However, a Circular Economy (CE) entails consideration of a complete life cycle of the product, including embodied impacts. Because sectoral data processing and energy requirements continue to grow, sustainable design and innovation in line with the 2020 EU Circular Economy Action Plan are crucial in promoting best practices and managing the sector’s energy demand in the future. This paper describes a proposed sustainable server design developed as part of the CEDaCI (a Circular Economy for the Data Centre Industry) project and based on the design requirements outlined by the EU Circular Economy Action Plan 2020 together with other directives. It presents recommendations for good practice methods, futureproofing data centre equipment design for a Circular Economy and building a critical foundation for sustainable hardware design that can be developed in the future.

Cooling in Poor Air Quality Environments—Impact of Fan Operation on Particle Deposition

Environmental pollutants are a source for reliability issues across data center and telecommunications equipment. A primary driver of this is the transport and deposition of particle matter (PM$_{2.5}$, PM$_{10}$) on printed circuit boards, electronic components and heat exchange surfaces. This process is enhanced by turbulent air flows generated from cooling fans. Particle pollutants can persist after contemporary filtering, highlighting the importance of elucidating particle transport mechanisms and utilising this information to design robust equipment. This study investigates particle transport behaviour arising from axial fans operating under varied aerodynamic conditions. Transient, multi-phase numerical simulations were performed to model the flow of millions of microscale particles in air and determine their fate. Across a comprehensive range of fan operation conditions, from aerodynamic stall to free delivery, non-dimensional deposition velocities spanned an order of magnitude. Deposition profiles vary from monotonic to non-monotonic behaviour, influenced by local flow impingement, blade tip vortices, and shear velocity. A simple flow control solution that mitigates the factors influencing deposition has been demonstrated for equipment already deployed. The findings and numerical methods can be applied for the optimization of fan-cooled equipment intended for indoor and outdoor environments where air quality is poor, or pollution levels are high.

Power Control Framework for Green Data Centers

In recent years, renewable energy, such as wind and photovoltaic electric power has been increasingly integrated into data center power provisioning systems to address high energy consumption of data centers. However, in reality, the intermittency and randomness of renewable energy (power supply fluctuation) is detrimental to the reliable operation of sophisticated IT equipment in those so-called green data centers. In this article, we address the problem of data center power regulation explicitly taking into account the unreliability and instability of renewable energy sources. To this extent, we design a novel data center power control framework that smoothens the power fluctuation and instability of renewable energy sources. The core of our framework is two power regulation optimization algorithms. In particular, a server workload scheduling algorithm deals with high frequency fluctuations while an Uninterruptable Power Supply (UPS) power regulation algorithm handles low frequency and large extent power fluctuations. These algorithms are also designed to satisfy service level agreement (SLA) and standby power supply capacity. We have conducted an extensive evaluation study using trace data of a real data center of 30000-node cluster with 50 x 250 UPS battery groups and 24-hour power generation data from real wind farm and photovoltaic power station. The experimental results show our framework effectively smoothens fluctuations of data center power supply, more effective use of renewable energy, and extend the UPS batteries’ lives to reduce the skyrocketed data center operating expenses.

Talking about the Management and Analysis of Intelligent Wiring in Data Center

For the application scenario of data center, the wiring management problem of data center equipment room is effectively solved through four purposes: visual wiring operation display, intelligent wiring selection construction scheme, automatic calculation of jumper length, and topology deduction of wiring link integrity. Eliminate the traditional operation and maintenance excel wiring ledger management method, eliminate the management pain points such as complicated wiring management, huge number of wiring, and inconsistent wiring ledger. In a visual way, make the wiring management intelligent, regular and efficient in a visual way. Select the start port and end port of the wiring, intelligently calculate all the connection paths of the wiring, and intelligently select the best wiring path for operation and maintenance personnel for reference. At present, the calculation method of the best path is the minimum number of device nodes. The jumper length of the route selected by the user is calculated automatically, and the wiring distance is calculated to facilitate the operation and maintenance personnel to select the suitable wiring length for construction. Support operation and maintenance personnel to quickly import construction information to generate construction orders. The intelligent wiring module supports the display and topology deduction verification of the global wiring in the equipment room, so as to check the integrity of the connection information of the upstream and downstream physical wiring of the current equipment in the form of topology, and supports the functions of hiding the distribution frame, changing the topology shape and saving. It makes the operation and maintenance personnel’s management of wiring in the equipment room simple and intuitive. The human-computer interaction mode can visually check the status of the equipment port occupation and all the device relationship sets passed by the entire wiring link.

Experimental and Computational Investigations of the Thermal Environment in a Small Operational Data Center for Potential Energy Efficiency Improvements

Abstract The share of equipment and power use in smaller data centers (DCs) is comparable with that of more massive counterparts. However, they grabbed less attention in the literature despite being less energy-efficient. This study highlights the challenges of setting up a computational fluid dynamics (CFD) model of a 180-m2 small-size high-performance computing (HPC) DC and the validation procedure leading to a reasonably accurate model for the investigation of the thermal environment and potential energy efficiency improvements. Leaky floors, uneven placement of computing equipment and perforated tiles preventing separation of hot and cold air, low-temperature operation, and excessive cooling capacity and fan power were identified sources of energy inefficiency in the DC. Computational fluid dynamics model predictions were gradually improved by using experimental measurements for various boundary conditions (BCs) and detailed geometrical representation of large leakage openings. Eventually, the model led to predictions with an error of less than 1 °C at the rack inlet and less than 5 °C at the rack outlet. The ultimate objective was to use the validated CFD model to test various energy efficiency measures in the form of operational or design changes in line with the best practices. Impact of leakage between the raised floor and the room, reduced airflow rate, cold-aisle and hot-aisle separation, workload consolidation, and higher temperature operation were among the phenomena tested by using the validated CFD model. The estimated power usage effectiveness (PUE) value reduced from 1.95 to 1.40 with the proposed energy efficiency measures.

Coupling energy efficiency and quality for consolidation of cloud workloads

Efficient usage of IT equipment in Data Centers requires modulating their power-consumption according to the actual workload. The most effective strategy to this aim consists in consolidating as many applications as possible on the smallest number of servers, so that idle devices can be shut down or put in low-power states. Usually, this process is driven by computing and networking resources requested by each application (e.g., CPU and RAM) and applies a certain degree of overcommitment, assuming that such resources are not fully used continuously. However, this approach is critical with real workload patterns, which usually change over time; as a matter of fact, consolidation in real scenarios often leads to either low efficiency or violations of Quality of Service (QoS) constraints, depending on the level of overcommitment.In this paper, we investigate a novel consolidation strategy based on an enhanced system model for the Infrastructure-as-a-Service cloud paradigm, which targets a better trade-off between Energy Efficiency and Quality of Service. We explicitly target modular cloud applications, which design is split into multiple components deployed in Virtual Machine (VM)s or containers. Our consolidation strategy allows to “freeze” parts of the application which are not currently used, making them available when requested with minimal latency. This improves energy saving with respect to other approaches, especially when idle VMs are present for backup or redundancy purposes, without degrading the service level. We compare multiple heuristics available in Optaplanner to solve our consolidation problem, and investigate improvements with respect to a more traditional approach. Our evaluation includes both simulations and experimentation in a real test-bed. The comparison shows that the Late Acceptance algorithm on average finds better solutions than other alternatives and energy efficiency improves up to 40% with respect to more conventional strategies, with deterioration of QoS indexes below 1%.

Maintenance of Electrical Equipment in Data Centers

Maintenance of Electrical Equipment in Data Centers

A Path Planning Method Based on Multi- Objective Cauchy Mutation Cat Swarm Optimization Algorithm for Navigation System of Intelligent Patrol Car

The intelligent patrol car with environmental sensing and autonomous navigation is a special robot, which is mostly used for equipment defect detection in industrial areas such as the power distribution room or data center room. A path planning algorithm for the navigation system of intelligent patrol car is proposed to ensure efficient and secure navigation in the complex indoor environment, and its effect is verified by simulation and experiment. First, a patrol car platform integrated with several intelligent devices is built to achieve global localization, mapping and path planning. Then a new co-optimization on multi-objective Cauchy mutation cat swarm optimization (MOCMCSO) and artificial potential field method (APFM) is proposed to solve the multi-objective optimization problem on shortest global path length and minimum total turning-angle variation. The optimal path is written into the navigation module to drive the patrol car to move and navigate. The simulations are carried out to confirm that the method can achieve a balance between the shortest path and good path smoothness, which has less optimization time and lower fitness value compared with multi-objective cat swarm optimization (MOCSO) and multi-objective particle swarm optimization (MOPSO), and is more suitable for global path planning in indoor environment. Finally, the experiments have been carried out in the data center equipment room to verify the effectiveness and superiority over the path planning algorithm on MOCMCSO.

A Simulation-Based Approach to Data Center Thermal Efficiency Optimization

Abstract The energy consumption for cooling electronic equipment in data centers using central systems is significant and will continue to rise. The motivation of the present research study is based on the need to determine optimization strategies to improve and optimize the thermal efficiency of data centers using a simulation-based approach. Here, simulation is used to model and optimize a proposed research data center for use as an environment to test equipment and investigate best practices and strategies such as containment and hybrid cooling. The optimization technique used in this study finds the optimal operating conditions and containment strategies of the data center while meeting specific thermal conformance criteria. More specifically, optimum supply airflow rate and temperature setpoint of cooling units are sought under different containment configurations, including both hot aisle and cold aisle containment strategies in both full and partial setups. The results of the computational fluid dynamics (CFD) simulations indicated a lower probability of hot spots with full hot aisle containment strategy in a data center operating at lower supply airflow rate and higher supply temperature setpoint. The optimization approach helped to determine a more efficient cooling system without the risk of under-provisioning. The study considered steady-state conditions with static heat load and fixed equipment layout. However, the generalized optimization process developed in the present study should add to the repertoire of tools presently used for the optimization of new air-cooled data centers.

Data center holistic demand response algorithm to smooth microgrid tie-line power fluctuation

With the rapid development of cloud computing, artificial intelligence technologies and big data applications, data centers have become widely deployed. High density IT equipment in data centers consumes a lot of electrical power, and makes data center a hungry monster of energy consumption. To solve this problem, renewable energy is increasingly integrated into data center power provisioning systems. Compared to the traditional power supply methods, renewable energy has its unique characteristics, such as intermittency and randomness. When renewable energy supplies power to the data center industrial park, this kind of power supply not only has negative effects on the normal operation of precision equipment, such as CPU/GPU chips and hard disk, in data center, but it would also impact the stability of the utility power grids operation. To solve this problem, this paper presents a novel tie-line power fluctuation smoothing algorithm with consideration of data center’s holistic demand response. The contributions of this paper are: (1) overcoming the limitations of treating IT load as uncontrollable workload in the traditional demand response research, we design a data center resource scheduling model to realize IT load demand response controllability; (2) two novel mechanisms are proposed: (i) the server cluster workload scheduling method with time shift mechanism, and (ii) the data center UPS (Uninterruptible Power Supply) energy storage dynamic response mechanism. (3) Combining these two mechanisms as holistic demand response of data center, we present a tie-line power fluctuation smoothing algorithm to improve power supply reliability, which is beneficial to both the high density and precision IT equipment in the data center and the utility power grid. In the experiments, the results show that the new algorithm can effectively regulate the tie-line power fluctuations under different server cluster utilization ranges and scenarios of large-scale penetration of distributed renewable energy scenarios. The new algorithm is hence able to contribute beneficially to the reliability and stability of intelligent industrial park micro-grid and utility power grids.

Real-time big data analytics for hard disk drive predictive maintenance

Effective and reliable cloud services depend on the quality of service provided by large-scale data centers, and data center equipment reliability issues can cause significant data and financial loss to cloud service clients. Corrective maintenance is a reactive approach that only corrects problems once they occur, resulting in unwanted downtime, while preventative maintenance relies on replacing equipment which may yet still have considerable effective operating lifetime, thus raising maintenance costs. In contrast, predictive maintenance can potentially predict equipment failure in advance, thus reducing unplanned downtime and extending equipment lifetimes, thus reducing maintenance costs while increasing system reliability. This research aims to develop a real-time predictive maintenance system, HDPass, based on Apache Spark for the detection of imminent hard disk drive (HDD) failures in data centers.

An AR Operation and Maintenance System for Data Center

This thesis proposes an operation and maintenance system based on Augmented Reality (AR) technology using in data center equipment room. It includes two subsystems: AR inspection, AR maintenance. The functions include data management during inspection and maintenance; intelligent guidance of inspection operations; all-round identification and display of equipment front-end and back-end data; data tracking and recording of the whole maintenance process; remote expert maintenance assist in real time. It effectively solves the practical problems of low efficiency in traditional operation and maintenance, missed inspection, and high labor costs in operation and maintenance. In addition, this thesis also describes a research case. A traditional operation and maintenance system without AR technology was developed to compare the user experience with the AR operation and maintenance system (AR-OM system). Through actual user feedback data, this thesis makes a detailed analysis of AR technology for data center inspection efficiency and user experience enhancement.

A simplified power consumption model of information technology (IT) equipment in data centers for energy system real-time dynamic simulation

Due to the rapid rise of power consumption of data centers in recent years, much work has been done to develop energy-efficient design, controls and diagnosis of their cooling systems, while the energy system simulation is used as an effective tool. However, existing models of information technology (IT) equipment of data centers cannot well represent the effects of IT equipment design and operation status on the data center cooling demand, and this hinders the development of the energy saving cooling technologies of data centers. To address this issue, this paper introduces a power consumption model of IT equipment in data centers with coefficients and modeling script provided for immediate use in data center energy system simulation. This energy model can be used to simulate energy performance of typical IT equipment in data centers under real-time dynamic operation conditions conveniently and effectively without the need of data other than the specifications of a data center design and IT equipment manuals. Its use with a commonly used building simulation program is demonstrated with a building model of a typical large office in a subtropical area. The results show that the model can represent the change of power consumption of data centers with different IT equipment designs and operation appropriately.

A Joint Power Efficient Server and Network Consolidation approach for virtualized data centers

Cloud computing and virtualization are enabling technologies for designing energy-aware resource management mechanisms in virtualized data centers. Indeed, one of the main challenges of big data centers is to decrease the power consumption, both to cut costs and to reduce the environmental impact. To this extent, Virtual Machine (VM) consolidation is often used to smartly reallocate the VMs with the objective of reducing the power consumption, by exploiting the VM live migration. The consolidation problem consists in finding the set of migrations that allow to keep turned on the minimum number of servers needed to host all the VMs. However, most of the proposed consolidation approaches do not consider the network related consumption, which represents about 10–20% of the total energy consumed by IT equipment in real data centers. This paper proposes a novel joint server and network consolidation model that takes into account the power efficiency of both the switches forwarding the traffic and the servers hosting the VMs. It powers down switch ports and routes traffic along the most energy efficient path towards the least energy consuming server under QoS constraints. Since the model is complex, a fast Simulated Annealing based Resource Consolidation algorithm (SARC) is proposed. Our numerical results demonstrate that our approach is able to save on average 50% of the network related power consumption compared to a network unaware consolidation.