green computing research paper 2020

An Analysis Report on Green Cloud Computing Current Trends and Future Research Challenges

Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur - India, February 26-28, 2019

8 Pages Posted: 12 Jun 2019

Archana Patil

Department of Computer Science and Engineering.PDA College of Engineering,Kalaburagi

Dr. Rekha Patil

PDA College of Engineering, Kalaburagi - Department of Computer Science and Engineering

Date Written: March 19, 2019

Today cloud computing became an impressive solution to address the challenges in storage and process of high volume data, with low-cost, high-speed, on-demand and pay-per-use characteristics. Although rapid progression has been recorded in the area of cloud computing and its services, attaining the implementation of green clouds is still under development due to lack of research and several barriers in its implementation. Green clouds are committed to design as eco-friendly, energy efficient, max resource utilizable, low carbon emissions, long lasting and recyclable. In order to satisfy the ever growing enterprise data storage and processing needs, the cloud service providers are coming up with cutting edge technologies like Green Cloud Computing in cloud architecture design to reduce, the huge power consumption, water consumption, need of physical hardware peripherals, infrastructure and harmful carbon emissions etc. To protect our environment from cloud negative impacts, the service providers must adopt and update their cloud infrastructure towards green computing. Green computing researches widely focus on designing of efficient clouds with green characteristics like Power Management, Virtualization, High Performance Computing, Load balancing, Green data center, Reusability, Recyclability etc. As part of my research on green clouds, this paper presents an analysis report about the green cloud computing and its characteristics in detailed manner. This paper thoroughly discusses about the former green computing achievements, current trending concepts of green computing and future research challenges as well. This comprehensive green cloud analysis report helps the naïve green research fellows to learn about green cloud topics and to understand the green cloud future research challenges.

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Department of Computer Science and Engineering.PDA College of Engineering,Kalaburagi ( email )

Aiwan-E-Shahi Area, Kalaburagi Karnataka, 585102 India

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Following the footprints of Bitcoins, many other cryptocurrencies were developed mostly adopting the same or similar Proof-of-Work (PoW) approach. Since completing the PoW puzzle requires extremely high computing power, consuming a vast amount of electricity, PoW has been strongly criticised for its antithetic stand against the notion of green computing. Use of application-specific hardware, particularly application-specific integrated circuits (ASICs) has further fuelled the debate, as these devices are of no use once they become “legacy” and hence obsolete to compete in the mining race, thus contributing to electronics waste. Therefore, this paper surveys the currently available alternative approaches to PoW and evaluates their applicability - especially their appropriateness in terms of greenness.

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Examining the environmental impact of computation and the future of green computing

When you think about your carbon footprint, what comes to mind? Driving and flying, probably. Perhaps home energy consumption or those daily Amazon deliveries. But what about watching Netflix or having Zoom meetings? Ever thought about the carbon footprint of the silicon chips inside your phone, smartwatch or the countless other devices inside your home?

Every aspect of modern computing, from the smallest chip to the largest data center comes with a carbon price tag. For the better part of a century, the tech industry and the field of computation as a whole have focused on building smaller, faster, more powerful devices — but few have considered their overall environmental impact.

Researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) are trying to change that.

“Over the next decade, the demand, number and types of devices is only going to grow,” said Udit Gupta, a Ph.D. candidate in Computer Science at SEAS. “We want to know what impact that will have on the environment and how we, as a field, should be thinking about how we adopt more sustainable practices.”

Gupta, along with Gu-Yeon Wei, the Robert and Suzanne Case Professor of Electrical Engineering and Computer Science, and David Brooks, the Haley Family Professor of Computer Science, will present a paper on the environmental footprint of computing at the IEEE International Symposium on High-Performance Computer Architecture on March 3, 2021.

The SEAS research is part of a collaboration with Facebook, where Gupta is an intern, and Arizona State University.

The team not only explored every aspect of computing, from chip architecture to data center design, but also mapped the entire lifetime of a device, from manufacturing to recycling, to identify the stages where the most emissions occur.

They found that most emissions related to modern mobile and data-center equipment come from hardware manufacturing and infrastructure.

“A lot of the focus has been on how we reduce the amount of energy used by computers, but we found that it’s also really important to think about the emissions from just building these processors,” said Brooks.  “If manufacturing is really important to emissions, can we design better processors? Can we reduce the complexity of our devices so that manufacturing emissions are lower?”

Take chip design, for example.

Today’s chips are optimized for size, performance and battery life. The typical chip is about 100 square millimeters of silicon and houses billions of transistors. But at any given time, only a portion of that silicon is being used. In fact, if all the transistors were fired up at the same time, the device would exhaust its battery life and overheat. This so-called dark silicon improves a device’s performance and battery life but it’s wildly inefficient if you consider the carbon footprint that goes into manufacturing the chip.

“You have to ask yourself, what is the carbon impact of that added performance,” said Wei. “Dark silicon offers a boost in energy efficiency but what’s the cost in terms of manufacturing? Is there a way to design a smaller and smarter chip that uses all of the silicon available? That is a really intricate, interesting, and exciting problem.”

The same issues face data centers. Today, data centers, some of which span many millions of square feet, account for 1 percent of global energy consumption, a number that is expected to grow.

As cloud computing continues to grow, decisions about where to run applications — on a device or in a data center — are being made based on performance and battery life, not carbon footprint.

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We need to be asking what’s greener, running applications on the device or in a data center,” said Gupta. “These decisions must optimize for global carbon emissions by taking into account application characteristics, efficiency of each hardware device, and varying power grids over the day.”

The researchers are also challenging industry to look at the chemicals used in manufacturing.  

Adding environmental impact to the parameters of computational design requires a massive cultural shift in every level of the field, from undergraduate CS students to CEOs.

To that end, Brooks has partnered with Embedded EthiCS , a Harvard program that embeds philosophers directly into computer science courses to teach students how to think through the ethical and social implications of their work. Brooks is including an Embedded EthiCS module on computational sustainability in “COMPSCI 146: Computer Architecture” this spring.

The researchers also hope to partner with faculty from Environmental Science and Engineering at SEAS and the Harvard University Center for the Environment to explore how to enact change at the policy level.

“The goal of this paper is to raise awareness of the carbon footprint associated with computing and to challenge the field to add carbon footprint to the list of metrics we consider when designing new processes, new computing systems, new hardware, and new ways to use devices. We need this to be a primary objective in the development of computing overall,” said Wei.

The paper was co-authored by Sylvia Lee, Jordan Tse, Hsien-Hsin S. Lee and Carole-Jean Wu from Facebook and Young Geun Kim from Arizona State University.

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Green Energy Research: Collaboration and Tools for a Sustainable Future

Science Article | Green Energy | 6 Sep 2024

The Urgency of Green Energy Innovation

The recent Climate Change 2023 synthesis report emphasizes the consequences of delayed emission reductions: fewer effective adaptation options for a warming planet 2 . Geopolitical factors like the Russia-Ukraine conflict further underscore the need for a green energy transition, with Europe’s energy security concerns highlighting the reliance on imported fossil fuels.

The Green Energy Research Landscape

Against this backdrop, green energy development has become a critical area of research, reflected in a more than 10-fold increase in related publications from 2010 (1,105) to 2023 (11,346), according to Digital Science’s Dimensions database. Researchers around the world are striving to improve green energy technology and society’s ability to harness renewable energy sources more efficiently.

According to data analysed by Nature Navigator , which uses artificial intelligence to generate comprehensive summaries of research topics, ‘renewable energy systems and technologies’ is the field’s most frequently mentioned subtopic (Fig.1). At a research concept level, wind power generation, grid optimization and resource management all feature as common underlying themes.

Figure 1: Topic anatomy of green energy research First-level nodes denote the research subtopic (highest prevalence themes emerging from green energy research). Second-level nodes denote the research concepts associated with these research subtopics. Note: only the research concepts mentioned in the highest count of outputs within each subtopic are presented here. Credit: Nature Research Intelligence

Of the primary green energy research subtopics presented by Nature Navigator , it is telling that ‘materials for energy storage and conversion’ is the fastest-growing, with a compound annual growth rate (CAGR) of 30.2% over the last five years. This may reflect a growing consensus among researchers and industry that a lack of options to efficiently store electricity generated by intermittent renewable sources for later use is a key bottleneck preventing the greater penetration of these sources into the grid.

Real-World Example: Accelerating Heat Pump Innovation

Changmo Sung, a prominent green energy researcher at Korea University, leveraged Nature Navigator to identify trends, key areas, and potential breakthroughs in heat pump technology. This facilitated a collaborative project with LG Electronics, accelerating their research efforts.

“It also enabled the rapid discovery of researchers and institutions outside Korea working on similar or complementary projects related to heat pumps” Sung says.

• International Energy Agency, Global Energy Review 2021 (2021).
• Intergovernmental Panel on Climate Change, Climate Change 2023 (2023).

Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam

Quantum Computational Intelligence Techniques: A Scientometric Mapping

• Review article
• Published: 07 September 2024

Cite this article

• Mini Arora 1   na1 &
• Kapil Gupta   ORCID: orcid.org/0000-0003-0264-948X 1   na1  

Computational intelligence has previously demonstrated its existence beyond the limitations of binary variables and Turing Machines. Using quantum concepts, Deutsch (1985) and Grover (1996) provide massive parallelism and searching techniques, vastly expanding the computational capacity of soft computing. This paper aims to analyze articles that consider both computational intelligence and quantum computing, referred to here as the quantum computational intelligence (QCI) category, to solve non-deterministic problems efficiently. The category includes 3067 research papers published from 2014 to 2023 that are indexed in high-quality databases like SCI and SCOPUS. This study examines QCI publishing patterns utilizing scientometric analysis employing co-occurrence, co-citation, and bibliographic coupling methodologies. Additionally, it provides insights into the citation patterns of publications, affiliations, and authors. China, USA, and India published more than half (53%) of the articles. The primary emphasis of application fields throughout this decade includes ‘Ground State Preparation’ and ‘Financial Forecasting’ among others. The pertinent keywords that have lately been studied are quantum particle swarm optimization (2022), optimization (2021), quantum circuits (2020), and deep learning (2019). Five quantum-based computation techniques were identified using a mix of critical review and cluster analysis: quantum machine learning, quantum neural networks, quantum particle swarm optimization, quantum variational Monte Carlo, and quantum-inspired evolutionary algorithms. The primary objective of this study is to address key queries that could contribute to future research in this field.

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This research was supported by the National Institute of Technology (NIT) Kurukshetra. The contributing author is a research scholar at NIT Kurukshetra and is receiving a scholarship from the institute.

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Mini Arora and Kapil Gupta have contributed equally to this work.

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Appendix 1: Different Measurement Terminology and Terms Used in This Scientometric Study

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Arora, M., Gupta, K. Quantum Computational Intelligence Techniques: A Scientometric Mapping. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10183-7

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Received : 14 February 2024

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Published : 07 September 2024

DOI : https://doi.org/10.1007/s11831-024-10183-7

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