Evaluation of Equipment Comfort in Data Centers by Fuzzy Logic

2013 ◽  
Vol 397-400 ◽  
pp. 1681-1684
Author(s):  
Chang Geng Yu ◽  
Gui Xiong Liu
Keyword(s):  
Fuzzy Logic ◽  
Problem Solving ◽  
Energy Saving ◽  
Mathematical Description ◽  
Data Center ◽  
Data Centers ◽  
Evaluation Method ◽  
Scientific Evaluation ◽  
Comfort Evaluation ◽  
Method Model

Evaluation of equipment comfort is the premise to ensure the equipment operating normally and the computer room energy-saving. Based on fuzzy logic, We present a equipment comfort evaluation method model FDEC which gives a mathematical description and a way to implementation and the scientific evaluation in Data center, equipment comfort is analyzed for three different perspectives: temperature, humidity and ventilation in the room. Evaluation of equipment comfort problem solving algorithm is designed.

scholarly journals DQN-based energy-efficient routing algorithm in software-defined data centers

2020 ◽  
Vol 16 (6) ◽  
pp. 155014772093577
Author(s):  
Zan Yao ◽  
Ying Wang ◽  
Xuesong Qiu
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Energy Saving ◽  
Heuristic Algorithm ◽  
Data Center ◽  
Energy Efficient ◽  
Data Centers ◽  
Routing Algorithm ◽  
Energy Efficient Routing ◽  
Saving Effect

With the rapid development of data centers in smart cities, how to reduce energy consumption and how to raise economic benefits and network performance are becoming an important research subject. In particular, data center networks do not always run at full load, which leads to significant energy consumption. In this article, we focus on the energy-efficient routing problem in software-defined network–based data center networks. For the scenario of in-band control mode of software-defined data centers, we formulate the dual optimal objective of energy-saving and the load balancing between controllers. In order to cope with a large solution space, we design the deep Q-network-based energy-efficient routing algorithm to find the energy-efficient data paths for traffic flow and control paths for switches. The simulation result reveals that the deep Q-network-based energy-efficient routing algorithm only trains part of the states and gets a good energy-saving effect and load balancing in control plane. Compared with the solver and the CERA heuristic algorithm, energy-saving effect of the deep Q-network-based energy-efficient routing algorithm is almost the same as the heuristic algorithm; however, its calculation time is reduced a lot, especially in a large number of flow scenarios; and it is more flexible to design and resolve the multi-objective optimization problem.

Research on Intelligent Air Conditioning System of Data Center

2014 ◽  
Vol 602-605 ◽  
pp. 928-932
Author(s):  
Min Li ◽  
Yun Wang ◽  
Zheng Qian Feng ◽  
Wang Li
Keyword(s):  
Heat Exchange ◽  
Energy Saving ◽  
Data Center ◽  
Air Conditioning ◽  
Data Centers ◽  
Cooling Efficiency ◽  
Exchange System ◽  
Air Conditioning System ◽  
Running Time ◽  
Heat Exchange System

By studying the energy-saving technologies of air-conditioning system in data centers, we designed a intelligent air conditioning system, improved the cooling efficiency of air conditioning system through a reasonable set of hot and cold aisles, reduced the running time of HVAC by using the intelligent heat exchange system, an provided a reference for energy saving research of air conditioning system of data centers.

scholarly journals Thermal Performance and Energy Saving Analysis of Indoor Air–Water Heat Exchanger Based on Micro Heat Pipe Array for Data Center

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 393 ◽  
Author(s):  
Heran Jing ◽  
Zhenhua Quan ◽  
Yaohua Zhao ◽  
Lincheng Wang ◽  
Ruyang Ren ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Heat Exchanger ◽  
Energy Saving ◽  
Heat Pipe ◽  
Flow Rate ◽  
Data Center ◽  
Data Centers ◽  
Air Flow ◽  
Air Flow Rate ◽  
Cold Energy

According to the temperature regulations and high energy consumption of air conditioning (AC) system in data centers (DCs), natural cold energy becomes the focus of energy saving in data center in winter and transition season. A new type of air–water heat exchanger (AWHE) for the indoor side of DCs was designed to use natural cold energy in order to reduce the power consumption of AC. The AWHE applied micro-heat pipe arrays (MHPAs) with serrated fins on its surface to enhance heat transfer. The performance of MHPA-AWHE for different inlet water temperatures, water and air flow rates was investigated, respectively. The results showed that the maximum efficiency of the heat exchanger was 81.4% by using the effectiveness number of transfer units (ε-NTU) method. When the max air flow rate was 3000 m3/h and the water inlet temperature was 5 °C, the maximum heat transfer rate was 9.29 kW. The maximum pressure drop of the air side and water side were 339.8 Pa and 8.86 kPa, respectively. The comprehensive evaluation index j/f1/2 of the MHPA-AWHE increased by 10.8% compared to the plate–fin heat exchanger with louvered fins. The energy saving characteristics of an example DCs in Beijing was analyzed, and when the air flow rate was 2500 m3/h and the number of MHPA-AWHE modules was five, the minimum payback period of the MHPA-AWHE system was 2.3 years, which was the shortest and the most economical recorded. The maximum comprehensive energy efficiency ratio (EER) of the system after the transformation was 21.8, the electric power reduced by 28.3% compared to the system before the transformation, and the control strategy was carried out. The comprehensive performance provides a reference for MHPA-AWHE application in data centers.

Energy Efficient Load Balancing in Cloud Data Center Using Clustering Technique

2019 ◽  
Vol 15 (1) ◽  
pp. 84-100 ◽  
Author(s):  
N. Thilagavathi ◽  
D. Divya Dharani ◽  
R. Sasilekha ◽  
Vasundhara Suruliandi ◽  
V. Rhymend Uthariaraj
Keyword(s):  
Response Time ◽  
Energy Saving ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Data Center ◽  
Resource Requirement ◽  
Server Consolidation ◽  
Cloud Data ◽  
Initial Allocation

Cloud computing has seen tremendous growth in recent days. As a result of this, there has been a great increase in the growth of data centers all over the world. These data centers consume a lot of energy, resulting in high operating costs. The imbalance in load distribution among the servers in the data center results in increased energy consumption. Server consolidation can be handled by migrating all virtual machines in those underutilized servers. Migration causes performance degradation of the job, based on the migration time and number of migrations. Considering these aspects, the proposed clustering agent-based model improves energy saving by efficient allocation of the VMs to the hosting servers, which reduces the response time for initial allocation. Middle VM migration (MVM) strategy for server consolidation minimizes the number of VM migrations. Further, randomization of extra resource requirement done to cater to real-time scenarios needs more resource requirements than the initial requirement. Simulation results show that the proposed approach reduces the number of migrations and response time for user request and improves energy saving in the cloud environment.

scholarly journals A Temperature-Risk and Energy-Saving Evaluation Model for Supporting Energy-Saving Measures for Data Center Server Rooms

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5222 ◽  
Author(s):  
Kosuke Sasakura ◽  
Takeshi Aoki ◽  
Masayoshi Komatsu ◽  
Takeshi Watanabe
Keyword(s):  
Energy Saving ◽  
Data Center ◽  
Data Centers ◽  
Prediction Models ◽  
Evaluation Model ◽  
High Heat ◽  
High Accuracy ◽  
Information And Communication ◽  
Saving Effect ◽  
Efficiency And Reliability

As data centers have become increasingly important in recent years their operational management must attain higher efficiency and reliability. Moreover, the power consumption of a data center is extremely large, and it is anticipated that it will continue to increase, so energy saving has become an urgent issue concerning data centers. In the meantime, the environment of the server rooms in data centers has become complicated owing to the introduction of virtualization technology, the installation of high-heat density information and communication technology (ICT) equipment and racks, and the diversification of cooling methods. It is very difficult to manage a server room in the case of such a complicated environment. When energy-saving measures are implemented in a server room with such a complicated environment, it is important to evaluate “temperature risks” in advance and calculate the energy-saving effect after the measures are taken. Under those circumstances, in this study, two prediction models are proposed: a model that predicts the rack intake temperature (so that the temperature risk can be evaluated in support of energy-saving measures implemented in the server room) and a model that evaluates the energy-saving effect (in relation to a baseline). Specifically, the models were constructed by using machine learning. The first constructed model evaluates the temperature risk in a verification room in advance, and it was confirmed that the model can evaluate the risk beforehand with high accuracy. The second constructed model—“baseline model” hereafter—supports energy-saving measures, and it was confirmed that the model can calculate the baseline (energy consumption) with high accuracy as well. Moreover, the effect of proposal process of energy-saving measures in the verification room was verified by using the two proposed models. In particular, the effectiveness of the model for evaluating temperature risk in advance and that of a technology for visualizing the energy-saving effect were confirmed.

Construction of Scientific Evaluation System for Energy Saving and Emission Reduction Oriented to Product Full Life-Cycle

2013 ◽  
Vol 291-294 ◽  
pp. 693-695 ◽  
Author(s):  
Lin Zhou ◽  
Jin Fu Xu ◽  
Xue Bo Xu
Keyword(s):  
Life Cycle ◽  
Energy Consumption ◽  
Energy Saving ◽  
Emission Reduction ◽  
Evaluation System ◽  
Product Life Cycle ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Fuzzy Evaluation ◽  
Scientific Evaluation

The widely used single assessment index such as energy consumption and main pollutants total emissions index can't comprehensively evaluation energy conservation and emission reduction. In this paper, a scientific comprehensive evaluation method of energy consumption based on the product life-cycle was proposed. This method fully consider industry characteristics at the same time, comprehensively use stratified fuzzy evaluation method. And the multi-objective colligation evaluation model and scientific evaluation system for energy saving and emission reduction oriented to industry characteristics was constructed. Finally, the empirical research was carried out take the heat treatment industry in Ningbo city as an example, and the result shows that this evaluation system is scientific and reasonable.

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.

scholarly journals Cold LOGIK and RDHX Solution for Data Center Energy Optimization

2018 ◽  
Vol 7 (3.4) ◽  
pp. 113
Author(s):  
T Suresh ◽  
Dr A. Murugan
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Data Center ◽  
Data Centers ◽  
Heat Dissipation ◽  
Cooling System ◽  
Cooling Power ◽  
Operational Cost ◽  
Power Cost ◽  
The Right

In all types of data center, keeping the right temperature with less cost and energy is one of important objective as energy saving is crucial in increased data driven industry. Energy saving is global focus for all industry. In Information technology, more than 60% of energy is utilized in data centers as it needs to be up and running. As per Avocent data center issue study, across globe more than 54% of data centers are in redesigning process to improve their efficiency and reduce operational cost and energy consumption. Data center managers and operators major challenge was how to maintain the temperature of servers with less power and energy. When the densities of data center energy nearing 5 kilowatts (kW) per cabinet, organizations are trying to find a way to manage the heat through latest technologies. Power usage per square can be reduced by incorporating liquid-cooling devices instead of increasing airflow volume. This is especially important in a data center with a typical under-floor cooling system. This research paper uses Rear-Door Heat eXchangers (RDHx) and cool logic solutions to reduce energy consumption. It gives result of implementation of Cold Logik and RDHx solution to Data center and proves that how it saves energy and power. Data center has optimized space, cooling, power and operational cost by implementing RDHx technology. This will enable to add more servers without increasing the space and reduce cooling and power cost. It also saves Data center space from heat dissipation from servers.  

scholarly journals Optimal Switching and Load Distribution Strategy for Energy Minimization and Performance Optimization of Cloud Data Center

2021 ◽  
Author(s):  
Poobalan A ◽  
◽  
Sangeetha S ◽  
Shanthakumar P ◽  
◽  
...  
Keyword(s):  
Energy Saving ◽  
Performance Optimization ◽  
Data Center ◽  
Load Distribution ◽  
Data Centers ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Power Load ◽  
Cloud Data Centers ◽  
Manta Ray

Cloud computing is a promising computing technology utilized in every stage of the business. The cloud offers different services to cloud users from anytime to anywhere, and it is attained with different parameters, like load optimization, resource optimization. Due to the increase in data center, energy consumption has become a major issue in green data centers. The majority of data centers are function using peak load with huge scales. Thus, it is essential for carrying out energy saving in cloud data centers. This paper designed an energy-saving method using fat tree. The proposed techniques optimize the load at different zones of data center and user in the cloud platform. Here, the distribution of load in cloud data centers is performed using Taylor-based Manta Ray Foraging Optimization (Taylor-MRFO), which is an integration of Manta Ray Foraging Optimization (MRFO) and Taylor series. The method utilized different objectives that involve power, load, latency, and bandwidth. With the load distribution, the switching of cloud data center to the desired mode is performed using Actor critic neural network (ACNN). Thus, the dual strategy leads to performance optimization in cloud infrastructure and also in consolidating parallel workload in data centers more effectively. The proposed Taylor-MRFO+ACNN outperformed other methods with minimal energy of 0.553, minimal load of 0.363, and minimal fitness of 0.437, respectively.

scholarly journals Towards Energy Efficiency in Data Centers: An Industrial Experience Based on Reuse and Layout Changes

2021 ◽  
Vol 11 (11) ◽  
pp. 4719
Author(s):  
Romulos da S. Machado ◽  
Fabiano dos S. Pires ◽  
Giovanni R. Caldeira ◽  
Felipe T. Giuntini ◽  
Flávia de S. Santos ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Saving ◽  
Data Center ◽  
Air Conditioning ◽  
Data Centers ◽  
Energy Resources ◽  
Cooling Power ◽  
Industrial Experience ◽  
Use Of Resources

Data centers are widely recognized for demanding many energy resources. The greater the computational demand, the greater the use of resources operating together. Consequently, the greater the heat, the greater the need for cooling power, and the greater the energy consumption. In this context, this article aims to report an industrial experience of achieving energy efficiency in a data center through a new layout proposal, reuse of previously existing resources, and air conditioning. We used the primary resource to adopt a cold corridor confinement, the increase of the raised floor’s height, and a better direction of the cold airflow for the aspiration at the servers’ entrance. We reused the three legacy refrigeration machines from the old data center, and no new ones were purchased. In addition to 346 existing devices, 80 new pieces of equipment were added (between servers and network assets) as a load to be cooled. Even with the increase in the amount of equipment, the implementations contributed to energy efficiency compared to the old data center, still reducing approximately 41% of the temperature and, consequently, energy-saving.

Sign in / Sign up

Export Citation Format

Share Document