Spatially-Aware Optimization of Energy Consumption in Consolidated Data Center Systems

2011 ◽  
Author(s):  
Hui Chen ◽  
Mukil Kesavan ◽  
Karsten Schwan ◽  
Ada Gavrilovska ◽  
Pramod Kumar ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Data Center ◽  
Power Distribution ◽  
Total Power ◽  
Inlet Temperature ◽  
Workload Management ◽  
Thermal Distribution ◽  
Total Power Consumption ◽  
Application Requirements ◽  
Online Methods

Energy efficiency in data center operation depends on many factors, including power distribution, thermal load and consequent cooling costs, and IT management in terms of how and where IT load is placed and moved under changing request loads. Current methods provided by vendors consolidate IT loads onto the smallest number of machines needed to meet application requirements. This paper’s goal is to gain further improvements in energy efficiency by also making such methods ‘spatially aware’, so that load is placed onto machines in ways that respect the efficiency of both cooling and power usage, across and within racks. To help implement spatially aware load placement, we propose a model-based reinforcement learning method to learn and then predict the thermal distribution of different placements for incoming workloads. The method is trained with actual data captured in a fully instrumented data center facility. Experimental results showing notable differences in total power consumption for representative application loads indicate the utility of a two-level spatially-aware workload management (SpAWM) technique in which (i) load is distributed across racks in ways that recognize differences in cooling efficiencies and (ii) within racks, load is distributed so as to take into account cooling effectiveness due to local air flow. The technique is being implemented using online methods that continuously monitor current power and resource usage within and across racks, sense BladeCenter-level inlet temperatures, understand and manage IT load according to an environment’s thermal map. Specifically, at data center level, monitoring informs SpAWM about power usage and thermal distribution across racks. At rack-level, SpAWM workload distribution is based on power caps provided by maximum inlet temperatures determined by CRAC speeds and supply air temperature. SpAWM can be realized as a set of management methods running in VMWare’s ESXServer virtualization infrastructure. Its use has the potential of attaining up to 32% improvements on the CRAC supply temperature requirement compared to non-spatially aware techniques, which can lower the inlet temperature 2∼3°C, that is to say we can increase the CRAC supply temperature 2∼3°C to save nearly 13% −18% cooling energy.

Performance Research on the All Aluminum Heat Exchanger of Family Air-Condition

2011 ◽  
Vol 71-78 ◽  
pp. 2068-2072
Author(s):  
Rui Wang ◽  
Yi Chun Wang ◽  
Chao Qing Feng ◽  
Huo Ming Zhan ◽  
Hua Jun Li
Keyword(s):  
Energy Efficiency ◽  
Heat Exchanger ◽  
Small Volume ◽  
Total Power ◽  
Efficiency Ratio ◽  
Total Power Consumption ◽  
The Common ◽  
Performance Research ◽  
Pipe Heat ◽  
The Ideal

Air enthalpy method is used in the contrastive experiment of the new condenser and the common wing-pipe heat exchanger of family air-condition. The refrigerating capacity and EER (Energy Efficiency Ratio) are obtained by the experiment. The conclusion of the experiment shows that the new condenser with small volume and diathermanous area can create more refrigerating capacity, but the total power consumption is basically unchanged, so the EER improved. This kind of all aluminum heat exchanger is the ideal substitute of family air-condition’s wing-pipe heat exchanger.

scholarly journals Joint Optimization of Pico-Base-Station Density and Transmit Power for an Energy-Efficient Heterogeneous Cellular Network

2019 ◽  
Vol 11 (10) ◽  
pp. 208
Author(s):  
Jie Yang ◽  
Ziyu Pan ◽  
Hengfei Xu ◽  
Han Hu
Keyword(s):  
Energy Efficiency ◽  
Focal Point ◽  
Transmission Rate ◽  
Optimization Method ◽  
Base Station ◽  
Base Stations ◽  
Joint Optimization ◽  
Total Power ◽  
Transmit Power ◽  
Total Power Consumption

Heterogeneous cellular networks (HCNs) have emerged as the primary solution for explosive data traffic. However, an increase in the number of base stations (BSs) inevitably leads to an increase in energy consumption. Energy efficiency (EE) has become a focal point in HCNs. In this paper, we apply tools from stochastic geometry to investigate and optimize the energy efficiency (EE) for a two-tier HCN. The average achievable transmission rate and the total power consumption of all the BSs in a two-tier HCN is derived, and then the EE is formulated. In order to maximize EE, a one-dimensional optimization algorithm is used to optimize picocell BS density and transmit power. Based on this, an alternating optimization method aimed at maximizing EE is proposed to jointly optimize transmit power and density of picocell BSs. Simulation results validate the accuracy of the theoretical analysis and demonstrate that the proposed joint optimization method can obviously improve EE.

scholarly journals Analysis and Design of Colpitts Oscillator for Short-range WSN

2016 ◽  
Vol 62 (3) ◽  
pp. 279-282
Author(s):  
Mousa Yousefi
Keyword(s):  
Energy Efficiency ◽  
Output Power ◽  
Short Range ◽  
Cmos Technology ◽  
Paper Analysis ◽  
Total Power ◽  
Colpitts Oscillator ◽  
Analysis And Design ◽  
Total Power Consumption ◽  
On Chip

Abstract In this paper, analysis and design of colpitts oscillator with ability to transmit data at low output power with application in short-range wireless sensor networks such as MICS is described. Reducing the area required to implement the transmitter, on-chip implementation and appropriate energy efficiency are the advantages of this structure that makes it suitable for the design of short-range transmitter in biomedical applications. The proposed OOK transmitter works at 405 MHz with 10 Mbps data rate. Output power and total power consumption are 25 µW and 726 µW, respectively. Energy efficiency is 72.6 pJ/bit. The transmitter has been designed and simulated in 0.18 µm CMOS technology.

A hierarchical energy efficiency optimization control strategy for distributed drive electric vehicles

2018 ◽  
Vol 233 (3) ◽  
pp. 605-621 ◽  
Author(s):  
Min Hua ◽  
Guoying Chen ◽  
Buyang Zhang ◽  
Yanjun Huang
Keyword(s):  
Energy Efficiency ◽  
Electric Vehicle ◽  
Sliding Mode ◽  
Fixed Ratio ◽  
Tracking Performance ◽  
Total Power ◽  
Control Allocation ◽  
Motor Efficiency ◽  
Total Power Consumption ◽  
Distributed Drive Electric Vehicle

Distributed drive electric vehicle with four in-wheel motors is widespread with various characteristics, such as performance potentials for independent wheel drive control and energy efficiency. However, in future, one of the biggest obstacles for its success in the automotive industry would be its limited energy storage. This paper proposes a hierarchical control method that involves a high-level motion controller that uses sliding mode control to calculate the total desired force and yaw moment and a low-level allocation controller in which an optimal energy-efficient control allocation scheme is presented to provide optimally distributed torques of four in-wheel motors in all the normal cases. A practicable motor energy efficiency model as a motor actuator is proposed by incorporating the electric motor efficiency map based on measured data into the motor efficiency experiment and a current closed-loop motor model. Moreover, both tracking performance and energy-saving are carried out in this research and evaluated via a co-simulation approach using MATLAB/Simulink and CarSim. A ramp maneuver at a constant speed and New European Driving Cycle and Urban Dynamometer Driving Schedule maneuvers have been conducted. To conclude, it is demonstrated that distributed drive electric vehicle with four in-wheel motors can reduce total power consumption and enhance tracking performance compared with a simple control allocation in which the torques are the fixed ratio distribution.

Improving Energy Efficiency in Data Centers by Controlling Task Distribution and Cooling

2018 ◽  
Author(s):  
Yusuke Nakajo ◽  
Jayati Athavale ◽  
Minami Yoda ◽  
Yogendra Joshi ◽  
Hiroaki Nishi
Keyword(s):  
Energy Efficiency ◽  
Load Balancing ◽  
Rapid Growth ◽  
Data Centers ◽  
Temperature Response ◽  
Total Power ◽  
Raspberry Pi ◽  
Total Power Consumption ◽  
Institute Of Technology ◽  
Global Energy Supply

The rapid growth in cloud computing, the Internet of Things (IoT), and data processing via Machine Learning (ML), have greatly increased our need for computing resources. Given this rapid growth, it is expected that data centers will consume more and more of our global energy supply. Improving their energy efficiency is therefore crucial. One of the biggest sources of energy consumption is the energy required to cool the data centers, and ensure that the servers stay within their intended operating temperature range. Indeed, about 40% of a data center’s total power consumption is for air conditioning[1]. Here, we study how the server air inlet and outlet, as well as the CPU, temperatures depend upon server loads typical of real Internet Protocol (IP) traces. The trace data used here are from Google clusters and include the times, job and task ID, as well as the number and usage of CPU cores. The resulting IT loads are distributed using standard load-balancing methods such as Round Robin (RR) and the CPU utilization method. Experiments are conducted in the Data Center Laboratory (DCL) at the Georgia Institute of Technology to monitor the server outlet air temperature, as well as real-time CPU temperatures for servers at different heights within the rack. Server temperatures were measured by on-line temperature monitoring with Xbee, Raspberry PI, Arduino, and hot-wire anemometers. Given that the temperature response varies with server position, in part due to spatial variations in the cooling airflow over the rack inlet and the server fan speeds, a new load-balancing approach that accounts for spatially varying temperature response within a rack is tested and validated in this paper.

scholarly journals A Dynamic Thermal-Allocation Solution to the Complex Economic Benefit for a Data Center

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hui Liu ◽  
Wenyu Song ◽  
Tianqi Jin ◽  
Zhiyong Wu ◽  
Fusheng Yan ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Economic Benefit ◽  
Data Centers ◽  
Economic Benefits ◽  
Dynamic Data ◽  
Workload Management ◽  
Profit Model ◽  
Thermal Distribution ◽  
Workload Allocation

Data centers, which provide computing services and gain profits, are indispensable to every city in the information era. They offer computation and storage while consuming energy and generate thermal discharges. To maximize the economic benefit, the existing research studies on the data center workload management mostly leverage the dynamical power model, i.e., the power-aware workload allocation. Nevertheless, we argue that for the complex relationship between the economic benefit and so many attributes, such as computation, energy consumption, thermal distribution, cooling, and equipment life, the thermal distribution dominates the others. Thus, thermal-aware workload allocation is more efficient. From the perspective of economic benefits, we propose a mathematical model for thermal distribution of a data center and study which workload distribution could determinately change the thermal distribution in the dynamic data center runtime, so as to reduce the cost and improve the economic benefits under the guarantee of service provisioning. By solving the thermal environment evaluation indexes, RHI (Return Heat Index) and RTI (Return Temperature Index), as well as heat dissipation models, we define quantitative models for the economic analysis such as energy consumption model for the busy servers and cooling, energy price model, and the profit model of data centers. Numerical simulation results validate our propositions and show that the average temperature of the data center reaches the best values, and the local hot spots are avoided effectively in various situations. As a conclusion, our studies contribute to the thermal management of the dynamic data center runtime for better economic benefits.

scholarly journals Energy Efficiency in 5G Communications – Conventional to Machine Learning Approaches

2020 ◽  
Vol 4 ◽  
pp. 1-9
Author(s):  
Muhammad Khalil Shahid ◽  
Filmon Debretsion ◽  
Aman Eyob ◽  
Irfan Ahmed ◽  
Tarig Faisal
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Data Exchange ◽  
Mixed Reality ◽  
Mimo Systems ◽  
Base Station ◽  
Small Cells ◽  
Total Power ◽  
5G Networks ◽  
Total Power Consumption

Demand for wireless and mobile data is increasing along with development of virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (ER) applications. In order to handle ultra-high data exchange rates while offering low latency levels, fifth generation (5G) networks have been proposed. Energy efficiency is one of the key objectives of 5G networks. The notion is defined as the ratio of throughput and total power consumption, and is measured using the number of transmission bits per Joule. In this paper, we review state-of-the-art techniques ensuring good energy efficiency in 5G wireless networks. We cover the base-station on/off technique, simultaneous wireless information and power transfer, small cells, coexistence of long term evolution (LTE) and 5G, signal processing algorithms, and the latest machine learning techniques. Finally, a comparison of a few recent research papers focusing on energy-efficient hybrid beamforming designs in massive multiple-input multiple-output (MIMO) systems is presented. Results show that machine learningbased designs may replace best performing conventional techniques thanks to a reduced complexity machine learning encoder

Energy Modeling of Air-Cooled Data Centers: Part I—The Optimization of Enclosed Aisle Configurations

2011 ◽  
Author(s):  
Dustin W. Demetriou ◽  
H. Ezzat Khalifa
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Temperature Rise ◽  
Data Center ◽  
Data Centers ◽  
Total Power ◽  
Optimum Operating ◽  
Cooling Power ◽  
Inlet Temperature ◽  
Total Energy Consumption

The work presented in this paper describes a simplified thermodynamic model that can be used for exploring optimization possibilities in air-cooled data centers. The model is used to parametrically evaluate the total energy consumption of the data center cooling infrastructure for data centers that utilize aisle containment. The analysis highlights the importance of reducing the total power required for moving the air within the CRACs, the plenum, and the servers, rather than focusing primarily or exclusively on reducing the refrigeration system’s power consumption and shows the benefits of bypass recirculation in enclosed aisle configurations. The analysis shows a potential for as much as a 57% savings in cooling infrastructure energy consumption by utilizing an optimized enclosed aisle configuration with bypass recirculation, instead of a traditional enclosed aisle, where all the data center exhaust is forced to flow through the computer room air conditioners (CRACs), for racks with a modest temperature rise (∼10°C). However, for racks with larger temperature rise (> ∼20°C), the saving are less than 5%. Furthermore, for servers whose fan speed (flow rate) varies as a function of inlet temperature, the analysis shows that the optimum operating regime for enclosed aisle data centers falls within a very narrow band and that power reductions are possible by lowering the uniform server inlet temperature in the enclosed aisle from 27°C to 22°C. However, the optimum CRAC exit temperature over the 22-to-27°C range of enclosed cold aisle temperature falls between ∼16 and 20°C because a significant reduction in the power consumption is possible through the use of bypass recirculation. Without bypass recirculation, the power consumption for a server inlet temperature of 22°C enclosed aisle case with a server temperature rise of 10°C would be a whopping 43% higher than with bypass recirculation. It is worth noting that, without bypass recirculation maintaining the enclosed cold aisle at 22°C instead of 27°C would reduce power consumption by 48%. It is also shown that enclosing the aisles together with bypass recirculation (when beneficial) also reduces the dependence of the optimum cooling power on server temperature rise.

THE ENERGY EFFICIENCY ANALYSIS OF DATA CENTER COOLING BASED ON SEPARATED HEAT PIPE

2018 ◽  
Author(s):  
Tao Wang ◽  
Yuhua Li ◽  
Huan Liu ◽  
Lei Zhang ◽  
Yuyan Jiang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Heat Pipe ◽  
Data Center ◽  
Efficiency Analysis ◽  
Data Center Cooling ◽  
Energy Efficiency Analysis

Improving the efficiency of oilfield electric power distribution

2019 ◽  
pp. 79-87
Author(s):  
Yu. F. Yu. F. Romaniuk ◽  
О. V. Solomchak ◽  
М. V. Hlozhyk
Keyword(s):  
Power Distribution ◽  
Reactive Power ◽  
Power Transmission ◽  
Transmission Efficiency ◽  
Active Power ◽  
Oil Field ◽  
Total Power ◽  
Simultaneous Increase ◽  
Active Load ◽  
Step Down

The issues of increasing the efficiency of electricity transmission to consumers with different nature of their load are considered. The dependence of the efficiency of the electric network of the oil field, consisting of a power line and a step-down transformer, on the total load power at various ratios between the active and reactive components of the power is analyzed, and the conditions under which the maximum transmission efficiency can be ensured are determined. It is shown by examples that the power transmission efficiency depends not only on the active load, but also largely on its reactive load. In the presence of a constant reactive load and an increase in active load, the total power increases and the power transmission efficiency decreases. In the low-load mode, the schedule for changing the power transmission efficiency approaches a parabolic form, since the influence of the active load on the amount of active power loss decreases, and their value will mainly depend on reactive load, which remains unchanged. The efficiency reaches its maximum value provided that the active and reactive components of the power are equal. In the case of a different ratio between them, the efficiency decreases. With a simultaneous increase in active and reactive loads and a constant value of the power factor, the power transmission efficiency is significantly reduced due to an increase in losses. With a constant active load and an increase in reactive load, efficiency of power transmission decreases, since with an increase in reactive load, losses of active power increase, while the active power remains unchanged. The second condition, under which the line efficiency will be maximum, is full compensation of reactive power.  Therefore, in order to increase the efficiency of power transmission, it is necessary to compensate for the reactive load, which can reduce the loss of electricity and the cost of its payment and improve the quality of electricity. Other methods are also proposed to increase the efficiency of power transmission by regulating the voltage level in the power center, reducing the equivalent resistance of the line wires, optimizing the loading of the transformers of the step-down substations and ensuring the economic modes of their operation.

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