Green Data Center

2014 ◽  
pp. 179-199 ◽  
Author(s):  
Bernardi Pranggono ◽  
Huaglory Tianfield
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Data Centers ◽  
Energy Aware ◽  
Political Issue ◽  
Future Directions ◽  
The World ◽  
Efficient Data ◽  
Green Data Center ◽  
Green Data Centers

The presence of computing in our society is enormous, and the trend continues. While organizations around the world rely more and more indispensably on their data centers to protect their fast growing data and information, the energy consumption of data center is becoming a key environmental, social, and political issue. Therefore, it is very important to minimize the energy usage by data centers. Green data centers refer to energy-aware data center design rationale of minimized carbon footprint. Virtualization has become a revolutionizing technology for systematic design and deployment of green data centers. This chapter presents a comprehensive study of green data centers. First, the concept and basic systems configuration of energy-efficient data centers are introduced. Then, an elaborative collection of greenness metrics are discussed for profiling the energy issues of green data centers. Next, the state-of-the-art energy-aware techniques are presented for the design and deployment of green data centers. Finally, the challenges and the future directions are pointed out.

Green Energy in Data Centers Using Internet of Things

2021 ◽  
pp. 225-246
Author(s):  
Vasaki Ponnusamy ◽  
Bobby Sharma ◽  
Gan Ming Lee
Keyword(s):  
Cloud Computing ◽  
Power Management ◽  
Data Center ◽  
Data Centers ◽  
High Capacity ◽  
Green Energy ◽  
Cooling Systems ◽  
Efficient Data ◽  
Green Data Center ◽  
Increasing Demand

Green energy infrastructure with the internet technologies relies on five important domains: green machine to machine (M2M), green cloud computing (CC), green data center (DC), green ICT, and green cellular. The ever-increasing demand for cloud computing and heavy dependence on cloud for storage, processing, and applications results in the need for more data centers with high capacity. Power management using wireless sensor networks (WSN) can be a potential solution as there has been a lot of works suing WSN for power management for green buildings, green home, and green farming. The same design can be applied to data centers with modifications to cater for data centers. Since WSN is part of IoT, various IoT-related solutions can be proposed for green data center solutions. A hybrid model that consists of virtualization, cooling systems, and IoT shows energy efficient data center designs. There have been various efforts as such, and this research will present green energy designs and mainly IoT-related initiatives for green-aware data centers.

scholarly journals Productive Efficiency of Energy-Aware Data Centers

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2053 ◽  
Author(s):  
Damián Fernández-Cerero ◽  
Alejandro Fernández-Montes ◽  
Francisco Velasco
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Data Center ◽  
Information Technologies ◽  
Data Centers ◽  
Large Data ◽  
Productive Efficiency ◽  
Energy Policies ◽  
Energy Aware ◽  
Constant Returns To Scale

Information technologies must be made aware of the sustainability of cost reduction. Data centers may reach energy consumption levels comparable to many industrial facilities and small-sized towns. Therefore, innovative and transparent energy policies should be applied to improve energy consumption and deliver the best performance. This paper compares, analyzes and evaluates various energy efficiency policies, which shut down underutilized machines, on an extensive set of data-center environments. Data envelopment analysis (DEA) is then conducted for the detection of the best energy efficiency policy and data-center characterization for each case. This analysis evaluates energy consumption and performance indicators for natural DEA and constant returns to scale (CRS). We identify the best energy policies and scheduling strategies for high and low data-center demands and for medium-sized and large data-centers; moreover, this work enables data-center managers to detect inefficiencies and to implement further corrective actions.

scholarly journals Energy-aware load balancing in distributed data centers

2020 ◽  
Author(s):  
Rodrigo A. C. Da Silva ◽  
Nelson L. S. Da Fonseca
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Virtual Machine ◽  
Data Center ◽  
Data Centers ◽  
Energy Savings ◽  
Virtual Machines ◽  
Distributed Data ◽  
Energy Aware ◽  
Cloud Data

This paper summarizes the dissertation ”Energy-aware load balancing in distributed data centers”, which proposed two new algorithms for minimizing energy consumption in cloud data centers. Both algorithms consider hierarchical data center network topologies and requests for the allocation of groups of virtual machines (VMs). The Topology-aware Virtual Machine Placement (TAVMP) algorithm deals with the placement of virtual machines in a single data center. It reduces the blocking of requests and yet maintains acceptable levels of energy consumption. The Topology-aware Virtual Machine Selection (TAVMS) algorithm chooses sets of VM groups for migration between different data centers. Its employment leads to relevant overall energy savings.

scholarly journals Energy aware dynamic virtual machine consolidation in cloud data centers

2018 ◽  
Vol 7 (2.8) ◽  
pp. 550 ◽  
Author(s):  
G Anusha ◽  
P Supraja
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Data Centers ◽  
Energy Aware ◽  
Large Power ◽  
Complex Tasks ◽  
Cloud Data ◽  
Network Bandwidth ◽  
Virtual Machine Consolidation ◽  
Cloud Data Centers

Cloud computing is a growing technology now-a-days, which provides various resources to perform complex tasks. These complex tasks can be performed with the help of datacenters. Data centers helps the incoming tasks by providing various resources like CPU, storage, network, bandwidth and memory, which has resulted in the increase of the total number of datacenters in the world. These data centers consume large volume of energy for performing the operations and which leads to high operation costs. Resources are the key cause for the power consumption in data centers along with the air and cooling systems. Energy consumption in data centers is comparative to the resource usage. Excessive amount of energy consumption by datacenters falls out in large power bills. There is a necessity to increase the energy efficiency of such data centers. We have proposed an Energy aware dynamic virtual machine consolidation (EADVMC) model which focuses on pm selection, vm selection, vm placement phases, which results in the reduced energy consumption and the Quality of service (QoS) to a considerable level.

Energy Consumption Analysis of a Hybrid Optimized Model for Sustainable Green Data Center

2021 ◽  
Author(s):  
Md. Alamgir Kabir ◽  
Shahadat Hossain ◽  
Mohammad Saiful Islam ◽  
Md. Mahmudul Hasan Imran ◽  
Mahmudur Rahman Akhanjee ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Energy Consumption Analysis ◽  
Optimized Model ◽  
Green Data Center

Energy-Efficiency in Cloud Data Centers

2013 ◽  
pp. 241-263
Author(s):  
Burak Kantarci ◽  
Hussein T. Mouftah
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Data Center ◽  
Energy Efficient ◽  
Large Scale ◽  
Data Centers ◽  
High Energy ◽  
It Services ◽  
Data Center Network ◽  
Cloud Data

Cloud computing aims to migrate IT services to distant data centers in order to reduce the dependency of the services on the limited local resources. Cloud computing provides access to distant computing resources via Web services while the end user is not aware of how the IT infrastructure is managed. Besides the novelties and advantages of cloud computing, deployment of a large number of servers and data centers introduces the challenge of high energy consumption. Additionally, transportation of IT services over the Internet backbone accumulates the energy consumption problem of the backbone infrastructure. In this chapter, the authors cover energy-efficient cloud computing studies in the data center involving various aspects such as: reduction of processing, storage, and data center network-related power consumption. They first provide a brief overview of the existing approaches on cool data centers that can be mainly grouped as studies on virtualization techniques, energy-efficient data center network design schemes, and studies that monitor the data center thermal activity by Wireless Sensor Networks (WSNs). The authors also present solutions that aim to reduce energy consumption in data centers by considering the communications aspects over the backbone of large-scale cloud systems.

Energy and SLA Efficient Virtual Machine Placement in Cloud Environment Using Non-Dominated Sorting Genetic Algorithm

2021 ◽  
pp. 773-789
Author(s):  
Oshin Sharma ◽  
Hemraj Saini
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Vm Placement ◽  
Energy Consumption Reduction ◽  
Consumption Reduction ◽  
The World ◽  
Placement Algorithm

To increase the availability of the resources and simultaneously to reduce the energy consumption of data centers by providing a good level of the service are one of the major challenges in the cloud environment. With the increasing data centers and their size around the world, the focus of the current research is to save the consumption of energy inside data centers. Thus, this article presents an energy-efficient VM placement algorithm for the mapping of virtual machines over physical machines. The idea of the mapping of virtual machines over physical machines is to lessen the count of physical machines used inside the data center. In the proposed algorithm, the problem of VM placement is formulated using a non-dominated sorting genetic algorithm based multi-objective optimization. The objectives are: optimization of the energy consumption, reduction of the level of SLA violation and the minimization of the migration count.

Minimization of VM Placement Change in Energy-Aware Resource Provisioning for Cloud Data Center

2013 ◽  
Vol 325-326 ◽  
pp. 1730-1733 ◽  
Author(s):  
Si Yuan Jing ◽  
Shahzad Ali ◽  
Kun She
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Network Flow ◽  
Resource Provisioning ◽  
Approximate Algorithm ◽  
Vm Placement ◽  
Energy Aware ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Placement Change

Numerous part of the energy-aware resource provision research for cloud data center just considers how to maximize the resource utilization, i.e. minimize the required servers, without considering the overhead of a virtual machine (abbreviated as a VM) placement change. In this work, we propose a new method to minimize the energy consumption and VM placement change at the same time, moreover we also design a network-flow-theory based approximate algorithm to solve it. The simulation results show that, compared to existing work, the proposed method can slightly decrease the energy consumption but greatly decrease the number of VM placement change

Solar Thermal Driven Cooling System for a Data Center in Albuquerque New Mexico

2010 ◽  
Author(s):  
N. Fumo ◽  
V. Bortone ◽  
J. C. Zambrano
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Data Centers ◽  
Primary Energy ◽  
Continuous Operation ◽  
Solar Thermal ◽  
Primary Energy Consumption ◽  
Solar Collectors ◽  
Absorption Chiller ◽  
Solar Cooling

Data centers are facilities that primarily contain electronic equipment used for data processing, data storage, and communications networking. Regardless of their use and configuration, most data centers are more energy intensive than other buildings. The continuous operation of Information Technology equipment and power delivery systems generates a significant amount of heat that must be removed from the data center for the electronic equipment to operate properly. Since data centers spend up to half their energy on cooling, cooling systems becomes a key factor for energy consumption reduction strategies and alternatives in data centers. This paper presents a theoretical analysis of an absorption chiller driven by solar thermal energy as cooling plant alternative for data centers. Source primary energy consumption is used to compare the performance of different solar cooling plants with a standard cooling plant. The solar cooling plants correspond to different combinations of solar collector arrays and thermal storage tank, with a boiler as source of energy to ensure continuous operation of the absorption chiller. The standard cooling plant uses an electric chiller. Results suggest that the solar cooling plant with flat-plate solar collectors is a better option over the solar cooling plant with evacuated-tube solar collectors. However, although solar cooling plants can decrease the primary energy consumption when compared with the standard cooling plant, the net present value of the cost to install and operate the solar cooling plants are higher than the one for the standard cooling plant.

scholarly journals An Artificial Neural Network Approach to Forecast the Environmental Impact of Data Centers

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 113 ◽  
Author(s):  
Joao Ferreira ◽  
Gustavo Callou ◽  
Albert Josua ◽  
Dietmar Tutsch ◽  
Paulo Maciel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Data Center ◽  
Data Centers ◽  
New Technologies ◽  
Operational Costs ◽  
The Us ◽  
Artificial Neural

Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO2 emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO2 emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO2 emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO2 emissions, Brazil would be the best option.

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