Server Racks Research Articles

Computational study of single-phase immersion cooling for high-energy density server rack for data centers

Computational study of single-phase immersion cooling for high-energy density server rack for data centers

Investigating thermal performance and energy efficiency in under-floor air distribution data centers: A case study

Investigating thermal performance and energy efficiency in under-floor air distribution data centers: A case study

Experimental study of an underground pipe gallery pre-cooled chimney ventilation system for a computer room

Experimental study of an underground pipe gallery pre-cooled chimney ventilation system for a computer room

Optimization of dielectric oils for immersion liquid cooling of server rack

Abstract In this research, two-phase immersion cooling is utilized to cool a server rack of 8 kW that contains 42 servers. Three-dimensional numerical simulation of server rack model is analyzed by ANSYS 18.1, Computational Fluid Dynamics (CFD). Comparative analysis between two types of dielectric engineered oils is performed, FC-72 and a new oil technology SF-33. The simulation shows a great decrease in temperature ranges for model using SF-33 oil with 34% decrease of the minimum temperature where the most surfaces of servers are at temperatures between 37 °C to 48 °C. Using SF-33 oil makes the hot spot temperature is higher than that for FC-72 by about 3°C. Power consumption for cooling with SF-33 oil is higher due to higher amount of vapor.

Power Packaging Trends in Emerging 48V Ecosystem

Recently, the semiconductor industry has seen significant technology and product development in application areas such as automotive, datacom, telecom and automation. System demands such as higher power density, higher switching efficiency and smaller form factors are becoming more important to reduce effective carbon footprint. Consequently, a new 48V system design has gained lot of attention. For example, the applicability of the 48V design/architecture was seen primarily in automotive mild hybrid cars and data center power delivery. In the world of automotive, degree of electrification, comfort features and advanced driver assistance systems (ADAS) in a car, result in need for higher total power budget. However, existing vehicle power tree is powered directly from a 12V lead acid battery to the mechanical auxiliary loads (typically <5-7 kW) such as water and oil pumps, climate compressor, active roll control, headlights and taillights. This, along with stricter emission norms from Corporate Average Fuel Economy (CAFE) standards and power hungry ADAS systems make it challenging to improve efficiency. Data centers offer services such as data storage, processing, networking and distribution. To manage these services, operators need power in the order of several 100’s of MW. As much as 40% of the operating costs of the data center comes from the energy needed to power and cool racks of servers. Amid the growing service demand, increasing efficiency and power density has become a priority for data center operators. On average, approximately 30-35% of power is wasted in the conversion from the AC grid to the microprocessors of an individual server. However, to realize system benefits, packaging technologies must not limit achievable electrical and thermal benefits. Historically, power device packaging has evolved from through hole packages such as TO247 and TO220 with long leads to surface mount components with shorter leads such as D2PAK, DPAK and SO8. Further, leaded packages have been replaced with lead-less surface mount options such as TOLL and PQFN, offering higher levels of integration. Package innovation has always been driven to improve parasitic resistances, inductances, thermal resistances and reliability. This presentation will explore some of the recent trends and new ideas in packaging technologies and their potential for addressing system performance requirements.

Estimation of natural convection heat transfer characteristics of rack server in a cavity: experimental and numerical analyzes

Estimation of natural convection heat transfer characteristics of rack server in a cavity: experimental and numerical analyzes

Dynamic Knowledge Management in an Agent-Based Extended Green Cloud Simulator

Cloud infrastructures operate in highly dynamic environments, and today, energy-focused optimization become crucial. Moreover, the concept of extended cloud infrastructure, which, among others, uses green energy, started to gain traction. This introduces a new level of dynamicity to the ecosystem, as “processing components” may “disappear” and “come back”, specifically in scenarios where the lack/return of green energy leads to shutting down/booting back servers at a given location. Considered use cases may involve introducing new types of resources (e.g., adding containers with server racks with “next-generation processors”). All such situations require the dynamic adaptation of “system knowledge”, i.e., runtime system adaptation. In this context, an agent-based digital twin of the extended green cloud infrastructure is proposed. Here, knowledge management is facilitated with an explainable Rule-Based Expert System, combined with Expression Languages. The tests were run using Extended Green Cloud Simulator, which allows the modelling of cloud infrastructures powered (partially) by renewable energy sources. Specifically, the work describes scenarios in which: (1) a new hardware resource is introduced in the system; (2) the system component changes its resource; and (3) system user changes energy-related preferences. The case study demonstrates how rules can facilitate control of energy efficiency with an example of an adaptable compromise between pricing and energy consumption.

Reliability analysis of CRAC system of a modern Data centre via Kriging algorithm

In the modern era of digital growth, Data centres serve as the important infrastructure of the interconnected global network. The Data centres perform as central hubs where immense volumes of data are processed, stored, and distributed. Reliable performance of data centres and Computer Room Air-Conditioning (CRAC) system is of critical importance, through which optimal environmental conditions are achieved. This paper proposes an interesting approach to evaluate the CRAC system’s ability to function properly under various uncertainties. Heat transfer model is developed using various environmental parameters, data server rack parameters, and evolving capacity of data servers. The uncertainties are modelled as random variables. One of the challenging issues in estimating the reliability of a system under uncertainties is to reduce the computational requirements while maintaining the accuracy. To overcome the issue, Kriging model is developed using adaptive sampling. The proposed approach is compared with the available approaches. The method shows better computational efficiency and accuracy.

TADRP: Toward Thermal-Aware Data Replica Placement in Data-Intensive Data Centers

With the mushrooming growth of data volumes, data replica placement plays a key role in promoting the energy efficiency and Quality-of-Service (QoS) of data-intensive data centers. The existing data placement strategies mainly focus on storage performance improvement or QoS enhancement in data centers, but ignore the indispensable factor -heat recirculation. To bridge this gap, we propose a thermal-aware data replica placement strategy called TADRP, aiming to improve cooling efficiency and minimize the total power consumption of data-intensive data centers. TADRP leverages an ant colony optimization (ACO) algorithm coupled with Laplacian probability distribution to find a quasi-optimal disk sequence (or Disk Sequence for short), which consists of disks selected from different rack servers to place data replicas. TADRP categorizes disks of Disk Sequence into active and inactive ones, by placing hot and cold replicas on active and inactive disks, respectively. We quantitatively evaluate TADRP in terms of cooling costs, total power consumption, number of power-state transitions, and execution time. We compare TADRP with four alternative solutions, namely, Random, Hadoop, SRS, and CDP-NSGAII. Experimental results show that TADRP can reduce the cooling costs and the total power of the existing solutions by 14.7% -61.7% and 19.2 %-55.1%, respectively, without undesirable I/O performance drops.

Design and analysis of wireless data center network topology HCDCN based on VLC

With the rapid development of the Internet of Things, big data, cloud computing and other industries, the data center network, as the core infrastructure of the computing industry, is becoming increasingly important. Wireless communication technology is developing rapidly. This paper uses visible light wireless communication technology to build a wireless data center network, solving the problems of complex wiring and expansion difficulties in the existing data center network.Aiming at the problems of insufficient spectrum, low bandwidth utilization and large energy consumption in the existing wireless data center, this paper adopts the visible light wireless communication technology based on LED, and proposes a new wireless data center network structure HCDCN. Firstly, work have been done to analyze the feasibility of introducing data centers in this technology, and transform server racks in combination with the characteristics of the technology. Secondly, the construction process of the structure is introduced and a new coding rule is proposed. In order to increase the routing efficiency and fault tolerance, the rack top link routing path is added, and an efficient routing algorithm is designed. Finally, the aggregate throughput of HCDCN under different scales is obtained through experiments, and the network performance of HCDCN structure is compared with that of OWCells and Mesh structure. Experiments have proved that HCDCN's network performance is better than the other two structures under the same conditions. The new wireless data center network proposed in this paper has largely alleviated the bandwidth shortage of hotspot servers.

Modeling Convective Heat Transfer of Air in a Data Center Using OpenFOAM

Achieving energy and cooling efficiency in data center convective air flow and heat transfer can be a challenging task. Among different numerical methods to work with such issues is the Finite Volume Method in Computational Fluid Dynamics. This work evaluates the performance of two such solvers provided by OpenFOAM® in solving this type of convective heat-transfer problem, namely BuoyantBoussinesqPimpleFOAM and BuoyantPimpleFOAM. This is done for two different flow configurations of significantly different Richardson number. To sufficiently resolve the flow, grid sizing effects are elucidated by way of the kernel density estimate. It determines the volume distribution of the temperature in the data center configuration. For the k-epsilon turbulence model used here, it was found that the compressible solver performs faster and requires less grid resolution for both flow configurations. This is attributed to the nature of the boundary conditions which are set such that the mass flow conservation per server rack and cooling unit is achieved. Transient solutions are found to provide better iterative convergence for cases that involves buoyancy, compressibility and flow separation. This is, in comparison to steady-state solutions where artificial numerical pressure drop is found, to depend on the momentum relaxation factors for the convective case with a higher Richardson number.

Modeling and Thermal Simulation of 2U-Rack Servers Applying Computational Fluid Dynamics Techniques

The data center industry consumes between 196 and 400 terawatt-hours annually, between 1% and 2% of the world's energy consumption. A high percentage of data center energy consumption is associated with air conditioning and cooling systems. The optimization of the data center cooling process can occur at multiple scales, being one of the most relevant to the generation and heat transfer processes within servers. In this approach, we must study the temperature fields of each server’s components and understand airflow behavior within the server chassis. The present work, in its first stage, performs a thermal and cooling analysis for a two-unit rack server (2U-Rack) using computational fluid dynamics (CFD) techniques via Ansys Fluent code. A geometric server model was developed, considering heat generation in the main electrical components and heat and air mass transfer with the data center. The model was validated by comparing simulation results with the server's sensor values and the results of thermography testing. Following CFD simulation, airflow velocity fields, temperature contours, and sensitivity scenarios were obtained, with the goal of proposing an optimization model that would allow us to improve server heat transfer processes in the following steps.

4 Ways to Put Lasers on Silicon: You Can Make Many Things with Silicon Photonics, But a Laser is not One of Them

Photonic Integrate Circuita, which combine a collection of optoelectronic functions on a single chip, are an increasingly common part of everyday life. They are used in high-speed optical transceivers that link server racks in data centers, including the one used to deliver the IEEE Spectrum website, in lidars to keep self-driving cars on track, and in spectrometers to spot chemicals in the atmosphere, among many other applications. All these systems have grown less expensive and, in some cases have become economically feasible, by making most of the IC with silicon fabrication technologies.

Impedance-Based Aggregation of Paralleled Power Factor Correction Converters in Data Centers

Existing aggregation methods for large-scale data center power systems often overlook disparities in power supply units, leading to reduced accuracy in the small signal dynamics of aggregated model. To fill this gap, this article develops an impedance-based aggregation method for paralleled power factor correction (PFC) converters with heterogeneities in power levels, conduction modes, and line impedances. The closed-loop input admittances of PFC converters operating in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) are presented first. The parametric scaling is then derived to aggregate multiple PFC converters at different power levels and operation modes, including the CCM, DCM, and mixed conduction mode, at the rack level. Furthermore, the aggregation rule and criteria for paralleled PFC converters with unequal line impedances is developed to provide a reduced-order dynamic model for adjacent server racks. The proposed parameter scaling and aggregation method allow efficient interaction analysis and stability assessment for data center power systems. The accuracy and robustness of the impedance-based aggregation are finally validated by simulations and experimental tests.

A two-phase heuristic algorithm for power-aware offline scheduling in IaaS clouds

A two-phase heuristic algorithm for power-aware offline scheduling in IaaS clouds

Numerical study on the optimal power distribution of server racks in a data center

Numerical study on the optimal power distribution of server racks in a data center

A data-driven subspace predictive control method for air-cooled data center thermal modelling and optimization

A data-driven subspace predictive control method for air-cooled data center thermal modelling and optimization

Fan-noise reduction of data centre telecommunications’ server racks, with an acoustic metamaterial broadband, low-frequency sound-absorbing liner

Fan-noise reduction of data centre telecommunications’ server racks, with an acoustic metamaterial broadband, low-frequency sound-absorbing liner

A Multi-Port Hardware Energy Meter System for Data Centers and Server Farms Monitoring.

Nowadays the rationalization of electrical energy consumption is a serious concern worldwide. Energy consumption reduction and energy efficiency appear to be the two paths to addressing this target. To achieve this goal, many different techniques are promoted, among them, the integration of (artificial) intelligence in the energy workflow is gaining importance. All these approaches have a common need: data. Data that should be collected and provided in a reliable, accurate, secure, and efficient way. For this purpose, sensing technologies that enable ubiquitous data acquisition and the new communication infrastructure that ensure low latency and high density are the key. This article presents a sensing solution devoted to the precise gathering of energy parameters such as voltage, current, active power, and power factor for server farms and datacenters, computing infrastructures that are growing meaningfully to meet the demand for network applications. The designed system enables disaggregated acquisition of energy data from a large number of devices and characterization of their consumption behavior, both in real time. In this work, the creation of a complete multiport power meter system is detailed. The study reports all the steps needed to create the prototype, from the analysis of electronic components, the selection of sensors, the design of the Printed Circuit Board (PCB), the configuration and calibration of the hardware and embedded system, and the implementation of the software layer. The power meter application is geared toward data centers and server farms and has been tested by connecting it to a laboratory server rack, although its designs can be easily adapted to other scenarios where gathering the energy consumption information was needed. The novelty of the system is based on high scalability built upon two factors. Firstly, the one-on-one approach followed to acquire the data from each power source, even if they belong to the same physical equipment, so the system can correlate extremely well the execution of processes with the energy data. Thus, the potential of data to develop tailored solutions rises. Second, the use of temporal multiplexing to keep the real-time data delivery even for a very high number of sources. All these ensure compatibility with standard IoT networks and applications, as the data markup language is used (enabling database storage and computing system processing) and the interconnection is done by well-known protocols.

Tuning block-parallel all-pairs shortest path algorithm for efficient multi-core implementa- tion.

Finding shortest paths in a weighted graph is one of the key problems in computer-science, which has numerous practical applications in multiple domains. This paper analyzes the parallel blocked all-pairs shortest path algorithm at the aim of evaluating the influence of the multi-core system and its hierarchical cache memory on the parameters of algorithm implementation depending on the size of the graph and the size of distance matrix’s block. It proposes a technique of tuning the block-size to the given multi-core system. The technique involves profiling tools in the tuning process and allows the increase of the parallel algorithm throughput. Computational experiments carried out on a rack server equipped with two Intel Xeon E5-2620 v4 processors of 8 cores and 16 hardware threads each have convincingly shown for various graph sizes that the behavior and parameters of the hierarchical cache memory operation don’t depend on the graph size and are determined only by the distance matrix’s block size. To tune the algorithm to the target multi-core system, the preferable block size can be found once for the graph size whose in-memory matrix representation is larger than the size of cache shared among all processor’s cores. Then this blocksize can be reused on graphs of bigger size for efficient solving the all-pairs shortest path problem