Analysis of AWG-Based Optical Data Center Switches

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Preeti Singh ◽  
J.K. Rai ◽  
Ajay K. Sharma
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Feedback Loop ◽  
Critical Role ◽  
Optical Data ◽  
Total Power ◽  
Switching Function ◽  
Packet Switches ◽  
Correct Operation ◽  
Loop Buffer

AbstractOptical packet switching (OPS) is an emerging area of research in next-generation datacenter applications. There is an exponential growth in the internet traffic due to applications such as social networking, cloud computing, video streaming, etc. Fiber optical network plays a critical role in various data center (DC) operations. Large bandwidth of the optical fiber made OPS, a promising technology for DCs. Arrayed waveguide gratings (AWGs) are used more frequently in the core of optical packet switches due to its low insertion loss and wavelength dependent routing pattern. Optical switching function can be performed by AWG if each input port is properly equipped with Tunable wavelength converters (TWCs). This paper presents analysis of four optical packet switches namely AWG and Feedback Loop Buffer based switch, AWG and Electronic Memory based switch, Petabit Optical Switch, AWG and Feedback Loop Buffer Loss Compensated based Switch. Switches are analyzed for Bit Error Rate (BER) and energy consumption. The power and noise analysis is performed for calculating the total power required for correct operation of switch and to determine the BER using physical layer analysis. Minimum power level is determined so that received bits are decoded correctly. Energy consumption per bit is also evaluated to identify architecture consuming less energy in the considered architectures.

scholarly journals A Parametric Numerical Study of the Airflow and Thermal Performance in a Real Data Center for Improving Sustainability

2019 ◽  
Vol 9 (18) ◽  
pp. 3850 ◽  
Author(s):  
Diogo Macedo ◽  
Radu Godina ◽  
Pedro Dinis Gaspar ◽  
Pedro da Silva ◽  
Miguel Trigueiros Covas
Keyword(s):  
Energy Consumption ◽  
Data Storage ◽  
Thermal Performance ◽  
Data Center ◽  
Data Centers ◽  
Numerical Study ◽  
Real Data ◽  
Ideal Gas ◽  
Three Dimensional Model ◽  
Correct Operation

In recent years, reducing energy consumption has been relentlessly pursued by researchers and policy makers with the purpose of achieving a more sustainable future. The demand for data storage in data centers has been steadily increasing, leading to an increase in size and therefore to consume more energy. Consequently, the reduction of the energy consumption of data center rooms is required and it is with this perspective that this paper is proposed. By using Computational Fluid Dynamics (CFD), it is possible to model a three-dimensional model of the heat transfer and air flow in data centers, which allows forecasting the air speed and temperature range under diverse conditions of operation. In this paper, a CFD study of the thermal performance and airflow in a real data center processing room with 208 racks under different thermal loads and airflow velocities is proposed. The physical-mathematical model relies on the equations of mass, momentum and energy conservation. The fluid in this study is air and it is modeled as an ideal gas with constant properties. The model of the effect of turbulence is made by employing a k–ε standard model. The results indicate that it is possible to reduce the thermal load of the server racks by improving the thermal performance and airflow of the data center room, without affecting the correct operation of the server racks located in the sensible regions of the room.

Optimization of Enclosed Aisle Data Centers Using Bypass Recirculation

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Dustin W. Demetriou ◽  
H. Ezzat Khalifa
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Energy Consumption ◽  
Data Center ◽  
Data Centers ◽  
Total Power ◽  
Total Energy Consumption ◽  
Air Conditioners ◽  
Data Center Cooling ◽  
Flow Through

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 evaluate parametrically 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 computer room air conditioners (CRACs), the plenum, and the servers, rather than focusing primarily or exclusively on reducing the refrigeration system’s power consumption. In addition, the benefits of introducing a bypass recirculation branch in enclosed aisle configurations are shown. The analysis shows a potential for as much as a 60% savings in cooling infrastructure energy consumption by utilizing an optimized enclosed aisle configuration with bypass recirculation, instead of a traditional enclosed aisle in which all the data center exhaust is forced to flow through the CRACs. Furthermore, computational fluid dynamics is used to evaluate practical arrangements for implementing bypass recirculation in raised floor data centers. A configuration where bypass tiles, with controllable low-lift fans, are placed close to the discharge of CRACs results in increased mixing and is shown to be a suitable method for providing nearly thermally uniform conditions to the inlet of the servers in an enclosed cold aisle. Other configurations of bypass implementation are also discussed and explored.

Energy-Saving in Optical Data Center Networks

2013 ◽  
Vol 411-414 ◽  
pp. 634-637
Author(s):  
Pei Pei Jiang ◽  
Cun Qian Yu ◽  
Yu Huai Peng
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Rapid Expansion ◽  
Optical Data ◽  
Data Center Networks ◽  
Vm Migration ◽  
Server Consolidation

In recent years, with the rapid expansion of network scale and types of applications, cloud computing and virtualization technology have been widely used in the data centers, providing a fast, flexible and convenient service. However, energy efficiency has increased dramatically. The problem of energy consumption has been widespread concern around the world. In this paper, we study the energy-saving in optical data center networks. First, we summarize the traditional methods of energy-saving and meanwhile reveal that the predominant energy consuming resources are the servers installed in the data centers. Then we present the server virtualization technologies based on Virtual Machines (VMs) that have been used widely to reduce energy consumption of servers. Results show server consolidation based on VM migration can efficiently reduce the overall energy consumption compared with traditional energy-saving approaches by reducing energy consumption of the entire network infrastructure in data center networks. For future work, we will study server consolidation based on VM migration in actual environment and address QoS requirements and access latency.

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.

scholarly journals A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning

2021 ◽  
Vol 11 (6) ◽  
pp. 2742
Author(s):  
Fatih Ünal ◽  
Abdulaziz Almalaq ◽  
Sami Ekici
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Prediction Models ◽  
Short Term Memory ◽  
Critical Role ◽  
Load Forecasting ◽  
Unified Framework ◽  
Short Term ◽  
Planning And Scheduling ◽  
Short Term Load Forecasting

Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers’ short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on data features coding and decoding is implemented in the prediction stage. The proposed approach is tested and validated using real-world datasets in Turkey, and the results outperformed the traditional prediction models compared in this paper.

scholarly journals Freely scalable and reconfigurable optical hardware for deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liane Bernstein ◽  
Alexander Sludds ◽  
Ryan Hamerly ◽  
Vivienne Sze ◽  
Joel Emer ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Data Transfer ◽  
Power Delivery ◽  
Optical Data ◽  
Optical Multicast ◽  
Optical Neural Network ◽  
Fully Connected ◽  
Management Power

AbstractAs deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are impeded by the costs of communication, thermal management, power delivery and clocking. To improve scalability, we propose a digital optical neural network (DONN) with intralayer optical interconnects and reconfigurable input values. The path-length-independence of optical energy consumption enables information locality between a transmitter and a large number of arbitrarily arranged receivers, which allows greater flexibility in architecture design to circumvent scaling limitations. In a proof-of-concept experiment, we demonstrate optical multicast in the classification of 500 MNIST images with a 3-layer, fully-connected network. We also analyze the energy consumption of the DONN and find that digital optical data transfer is beneficial over electronics when the spacing of computational units is on the order of $$>10\,\upmu $$ > 10 μ m.

scholarly journals Nanoscale optical writing through upconversion resonance energy transfer

2021 ◽  
Vol 7 (9) ◽  
pp. eabe2209
Author(s):  
S. Lamon ◽  
Y. Wu ◽  
Q. Zhang ◽  
X. Liu ◽  
M. Gu
Keyword(s):  
Graphene Oxide ◽  
Energy Transfer ◽  
Energy Consumption ◽  
Data Storage ◽  
High Capacity ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
High Energy ◽  
Optical Data ◽  
Upconversion Nanoparticles

Nanoscale optical writing using far-field super-resolution methods provides an unprecedented approach for high-capacity data storage. However, current nanoscale optical writing methods typically rely on photoinitiation and photoinhibition with high beam intensity, high energy consumption, and short device life span. We demonstrate a simple and broadly applicable method based on resonance energy transfer from lanthanide-doped upconversion nanoparticles to graphene oxide for nanoscale optical writing. The transfer of high-energy quanta from upconversion nanoparticles induces a localized chemical reduction in graphene oxide flakes for optical writing, with a lateral feature size of ~50 nm (1/20th of the wavelength) under an inhibition intensity of 11.25 MW cm−2. Upconversion resonance energy transfer may enable next-generation optical data storage with high capacity and low energy consumption, while offering a powerful tool for energy-efficient nanofabrication of flexible electronic devices.

Reducing energy consumption in SDN-based data center networks through flow consolidation strategies

2019 ◽  
Author(s):  
Marcelo da Silva Conterato ◽  
Tiago Coelho Ferreto ◽  
Fábio Rossi ◽  
Wagner dos Santos Marques ◽  
Paulo Silas Severo de Souza
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Data Center Networks ◽  
Through Flow ◽  
Reducing Energy

scholarly journals Energy Recovery Potential in Industrial and Municipal Wastewater Networks Using Micro-Hydropower in Spain

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 691
Author(s):  
Aida Mérida García ◽  
Juan Antonio Rodríguez Díaz ◽  
Jorge García Morillo ◽  
Aonghus McNabola
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Energy Recovery ◽  
Energy Production ◽  
Municipal Wastewater ◽  
Distribution Networks ◽  
Total Power ◽  
Energy Potential ◽  
Total Energy Consumption ◽  
Recovery Potential

The use of micro-hydropower (MHP) for energy recovery in water distribution networks is becoming increasingly widespread. The incorporation of this technology, which offers low-cost solutions, allows for the reduction of greenhouse gas emissions linked to energy consumption. In this work, the MHP energy recovery potential in Spain from all available wastewater discharges, both municipal and private industrial, was assessed, based on discharge licenses. From a total of 16,778 licenses, less than 1% of the sites presented an MHP potential higher than 2 kW, with a total power potential between 3.31 and 3.54 MW. This total was distributed between industry, fish farms and municipal wastewater treatment plants following the proportion 51–54%, 14–13% and 35–33%, respectively. The total energy production estimated reached 29 GWh∙year−1, from which 80% corresponded to sites with power potential over 15 kW. Energy-related industries, not included in previous investigations, amounted to 45% of the total energy potential for Spain, a finding which could greatly influence MHP potential estimates across the world. The estimated energy production represented a potential CO2 emission savings of around 11 thousand tonnes, with a corresponding reduction between M€ 2.11 and M€ 4.24 in the total energy consumption in the country.

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