Lifetime Exergy Consumption as a Sustainability Metric for Enterprise Servers

2008 ◽  
Author(s):  
Christopher R. Hannemann ◽  
Van P. Carey ◽  
Amip J. Shah ◽  
Chandrakant Patel
Keyword(s):  
Data Centers ◽  
Large Data ◽  
Cooling Power ◽  
Processing Power ◽  
Dynamic Cooling ◽  
Exergy Consumption ◽  
Consumption Model ◽  
Processing And Storage ◽  
The Impact ◽  
And Storage

As the use of information technology becomes more ubiquitous, the need for data processing and storage capabilities increases. This results in the construction and operation of large data centers—facilities that house thousands of servers and serve as the backbone for all types of computational processes. Unfortunately, as processing power and storage capacity increases, so does the corresponding power and cooling requirements of the data centers. Several studies have examined the efficiency of data centers by focusing on server and cooling power inputs, but this fails to capture the data center’s entire impact. To accomplish this, the use of a lifetime exergy (available energy) analysis is proposed. This study first details the development of a lifetime exergy consumption model designed specifically for data center analysis. To create a database of computer components, a disassembly analysis was performed, and the results are detailed. By combining the disassembly analysis of a server with the aggregation of energy and material data, a more rigorous and useful assessment of the server’s overall impact is demonstrated. The operation of the lifetime exergy consumption model is demonstrated by case studies examining the effects of variance in transportation and cooling strategies. The importance of transportation modes and material mass, which are greatly affected by supply chain parameters, is shown. The impact of static and dynamic cooling within data centers is also demonstrated.

scholarly journals A Hyperconnected Smart City Framework

2020 ◽  
Vol 24 ◽  
Author(s):  
Naureen Naqvi ◽  
Sabih Ur Rehman ◽  
Zahidul Islam
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Security And Privacy ◽  
Directed Network ◽  
Web Based ◽  
Sustainable Environment ◽  
Processing And Storage ◽  
The Impact ◽  
And Storage

Recent technological advancements have given rise to the concept of hyper-connected smart cities being adopted around the world. These cities aspire to achieve better outcomes for citizens by improving the quality of service delivery, information sharing, and creating a sustainable environment. A smart city comprises of a network of interconnected devices also known as IoT (Internet of Things), which captures data and transmits it to a platform for analysis. This data covers a variety of information produced in large volumes also known as Big Data. From data capture to processing and storage, there are several stages where a breach in security and privacy could result in catastrophic impacts. Presently there is a gap in the centralization of knowledge to implement smart city services with a secure architecture. To bridge this gap, we present a framework that highlights challenges within the smart city applications and synthesizes the techniques feasible to solve them. Additionally, we analyze the impact of a potential breach on smart city applications and state-of-the-art architectures available. Furthermore, we identify the stakeholders who may have an interest in learning about the relationships between the significant aspects of a smart city. We demonstrate these relationships through force-directed network diagrams. They will help raise the awareness amongst the stakeholders for planning the development of a smart city. To complement our framework, we designed web-based interactive resources that are available from http://ausdigitech.com/smartcity/.

Collaborative Filtering Based Data Mining for Large Data

2017 ◽  
pp. 115-127
Author(s):  
Amrit Pal ◽  
Manish Kumar
Keyword(s):  
Data Mining ◽  
Collaborative Filtering ◽  
Large Data ◽  
Map Reduce ◽  
Huge Amount ◽  
Distributed Models ◽  
Programming Framework ◽  
Processing And Storage ◽  
And Storage ◽  
Filtering Techniques

Size of data is increasing, it is creating challenges for its processing and storage. There are cluster based techniques available for storage and processing of this huge amount of data. Map Reduce provides an effective programming framework for developing distributed program for performing tasks which results in terms of key value pair. Collaborative filtering is the process of performing recommendation based on the previous rating of the user for a particular item or service. There are challenges while implementing collaborative filtering techniques using these distributed models. Some techniques are available for implementing collaborative filtering techniques using these models. Cluster based collaborative filtering, map reduce based collaborative filtering are some of these techniques. Chapter addresses these techniques and some basics of collaborative filtering.

Microgrids: A New Approach to Supply-Side Design for Data Centers

2009 ◽  
Author(s):  
Ratnesh Sharma ◽  
Cullen Bash ◽  
Manish Marwah ◽  
Chandrakant Patel ◽  
Tom Christian
Keyword(s):  
Power Generation ◽  
Data Centers ◽  
Large Data ◽  
Cost Effective ◽  
Operational Reliability ◽  
Supply Side ◽  
It Infrastructure ◽  
Environmental Footprint ◽  
Evolutionary Changes ◽  
The Impact

Growth in IT infrastructure driven by socio-economic demand for services has led to the creation of large data centers. There is a need for cost-effective and sustainable design and management of such data centers. From this perspective, evolutionary changes in the regulatory and operational climate of traditional electrical and energy utilities has created new opportunities for development of data centers with low TCO and environmental footprint. These opportunities primarily exist on developing unique supply-side architectures for delivery of power, water and other resources to service data centers. Concurrent emergence of smaller heat and power generating systems also provides novel options to create solutions that improve the reliability and scalability of supply-side infrastructures in data centers. In this paper we investigate the impact of combined heat and power generation in operation of data centers in reducing TCO and environmental footprint and improving operational reliability. Usage of natural resources like water, fuel is minimized to create a low footprint IT infrastructure. Through use of mix of on-site power generation technologies alongside energy and water storage we create a power, cooling and water microgrid for the data center. Such microgrids are a promising way to capture the significant potential of smaller distributed energy resources to meet growing demands for low footprint IT infrastructures.

scholarly journals CFD Optimization of the Cooling of Yosemite Open Compute Server

2017 ◽  
Author(s):  
Aditya Gupta ◽  
Ananthavijayan Sridhar ◽  
Dereje Agonafer
Keyword(s):  
Energy Consumption ◽  
Data Centers ◽  
Air Flow ◽  
Flow Characteristics ◽  
Cost Savings ◽  
Cooling Power ◽  
Original Design ◽  
Flow Optimization ◽  
Cooling Performance ◽  
The Impact

Over the past few years, there has been an ever increasing rise in energy consumption by IT equipment in Data Centers. Thus, the need to minimize the environmental impact of Data Centers by optimizing energy consumption and material use is increasing. In 2011, the Open Compute Project was started which was aimed at sharing specifications and best practices with the community for highly energy efficient and economical data centers. The first Open Compute Server was the ‘ Freedom’ Server. It was a vanity free design and was completely custom designed using minimum number of components and was deployed in a data center in Prineville, Oregon. Within the first few months of operation, considerable amount of energy and cost savings were observed. Since then, progressive generations of Open Compute servers have been introduced. Initially, the servers used for compute purposes mainly had a 2 socket architecture. In 2015, the Yosemite Open Compute Server was introduced which was suited for higher compute capacity. Yosemite has a system on a chip architecture having four CPUs per sled providing a significant improvement in performance per watt over the previous generations. This study mainly focuses on air flow optimization in Yosemite platform to improve its overall cooling performance. Commercially available CFD tools have made it possible to do the thermal modeling of these servers and predict their efficiency. A detailed server model is generated using a CFD tool and its optimization has been done to improve the air flow characteristics in the server. Thermal model of the improved design is compared to the existing design to show the impact of air flow optimization on flow rates and flow speeds which in turn affects CPU die temperatures and cooling power consumption and thus, impacting the overall cooling performance of the Yosemite platform. Emphasis is given on effective utilization of fans in the server as compared to the original design and improving air flow characteristics inside the server via improved ducting.

scholarly journals Modeling and Fault Tolerance Analysis of ZigBee Protocol in IoT Networks

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8264
Author(s):  
Paweł Dymora ◽  
Mirosław Mazurek ◽  
Krzysztof Smalara
Keyword(s):  
Fault Tolerance ◽  
Routing Algorithm ◽  
Tolerance Analysis ◽  
Sensor Nodes ◽  
Zigbee Protocol ◽  
Battery Degradation ◽  
Processing And Storage ◽  
The Right ◽  
The Impact ◽  
And Storage

This paper presents the essence of IoT (Internet of Things) works and design challenges, discusses its principles of operation, and presents IoT development concepts. WSN (Wireless Sensor Network) was characterized in detail as an essential component of IoT infrastructure. The various faults that can occur at all levels of the IoT architecture, such as sensor nodes, actuators, network links, as well as processing and storage components clearly demonstrate that fault-tolerance (FT) has become a key issue for IoT systems. A properly applied routing algorithm has a direct impact on the power consumption of sensors, which in extreme cases is the reason why nodes shut down due to battery degradation. To study the fault tolerance of IoT infrastructure, a ZigBee network topology was created, and various node failure scenarios were simulated. Furthermore, the results presented showed the impact and importance of choosing the right routing scheme, based on the correlation of throughput to the number of rejected packets, as well as the proportionality of the value of management traffic to the other including the ratio of rejected packets.

HUBUNGAN HIGIENE SANITASI DENGAN KANDUNGAN ESCHERICHIA COLI PADA ES BATU INDUSTRI RUMAH TANGGA DI TEPI JALAN WAHID HASYIM 2 KECAMATAN SEMPAJA KOTA SAMARINDA TAHUN 2015

2018 ◽  
Vol 2 (1) ◽  
pp. 43
Author(s):  
Suwignyo Suwignyo ◽  
Abdul Rachim ◽  
Arizal Sapitri
Keyword(s):  
Escherichia Coli ◽  
Data Analysis ◽  
Random Sampling ◽  
Preliminary Test ◽  
Raw Material ◽  
Almost Everywhere ◽  
Ice Cube ◽  
Processing And Storage ◽  
And Storage ◽  
Cluster Random Sampling

Ice is a water that cooled below 0 °C and used for complement in drink. Ice can be found almost everywhere, including in the Wahid Hasyim Sempaja Roadside. From the preliminary test, obtained 5 samples ice cube were contaminated by Escherichia coli. The purpose of this study was to determine relationship between hygiene and sanitation with presence of Eschericia coli in ice cube of home industry at Wahid Hasyim Roadside Samarinda. This research used quantitative with survey methode. The population in this study was all of the seller in 2nd Wahid Hasyim Roadside. Sample was taken by Krejcie and Morgan so the there were 44 samples and used Cluster Random Sampling. The instruments are questionnaries, observation and laboratory test. Data analysis was carried out univariate and bivariate (using Fisher test p= 0.05). The conclusion of this study there are a relation between chosing raw material (p=0,03) and saving raw material (p=0,03) with presence of Eschericia coli. There was no relation between processing raw material into ice cube with presence of Eschericia coli (p=0,15).Advice that can be given to ice cube should maintain hygiene and sanitation of the selection, processing and storage of ice cube.

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