research paper on applications of green computing

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• Published: 26 June 2023

GREENER principles for environmentally sustainable computational science

• Loïc Lannelongue   ORCID: orcid.org/0000-0002-9135-1345 1 , 2 , 3 , 4 ,
• Hans-Erik G. Aronson   ORCID: orcid.org/0000-0002-1702-1671 5 ,
• Alex Bateman 6 ,
• Ewan Birney 6 ,
• Talia Caplan   ORCID: orcid.org/0000-0001-8990-1435 7 ,
• Martin Juckes   ORCID: orcid.org/0000-0003-1770-2132 8 ,
• Johanna McEntyre 6 ,
• Andrew D. Morris 5 ,
• Gerry Reilly 5 &
• Michael Inouye 1 , 2 , 3 , 4 , 9 , 10 , 11  

Nature Computational Science volume  3 ,  pages 514–521 ( 2023 ) Cite this article

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The carbon footprint of scientific computing is substantial, but environmentally sustainable computational science (ESCS) is a nascent field with many opportunities to thrive. To realize the immense green opportunities and continued, yet sustainable, growth of computer science, we must take a coordinated approach to our current challenges, including greater awareness and transparency, improved estimation and wider reporting of environmental impacts. Here, we present a snapshot of where ESCS stands today and introduce the GREENER set of principles, as well as guidance for best practices moving forward.

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Scientific research and development have transformed and immeasurably improved the human condition, whether by building instruments to unveil the mysteries of the universe, developing treatments to fight cancer or improving our understanding of the human genome. Yet, science can, and frequently does, impact the environment, and the magnitude of these impacts is not always well understood. Given the connection between climate change and human health, it is becoming increasingly apparent to biomedical researchers in particular, as well as their funders, that the environmental effects of research should be taken into account 1 , 2 , 3 , 4 , 5 .

Recent studies have begun to elucidate the environmental impacts of scientific research, with an initial focus on scientific conferences and experimental laboratories 6 . The 2019 Fall Meeting of the American Geophysical Union was estimated to emit 80,000 metric tonnes of CO 2 equivalent (tCO 2 e), equivalent to the average weekly emissions of the city of Edinburgh, UK 7 (CO 2 e, or CO 2 -equivalent, summarizes the global warming impacts of a range of greenhouse gases (GHGs) and is the standard metric for carbon footprints, although its accuracy is sometimes debated 8 ) The annual meeting of the Society for Neuroscience was estimated to emit 22,000 tCO 2 e, approximately the annual carbon footprint of 1,000 medium-sized laboratories 9 . The life-cycle impact (including construction and usage) of university buildings has been estimated at ~0.125 tCO 2 e m −2  yr −1 (ref. 10 ), and the yearly carbon footprint of a typical life-science laboratory at ~20 tCO 2 e (ref. 9 ). The Laboratory Efficiency Assessment Framework (LEAF) is a widely adopted standard to monitor and reduce the carbon footprint of laboratory-based research 11 . Other recent frameworks can help to raise awareness: GES 1point5 12 provides an open-source tool to estimate the carbon footprint of research laboratories and covers buildings, procurement, commuting and travel, and the Environmental Responsibility 5-R Framework provides guidelines for ecologically conscious research 13 .

With the increasing scale of high-performance and cloud computing, the computational sciences are susceptible to having silent and unintended environmental impacts. The sector of information and communication technologies (ICT) was responsible for between 1.8% and 2.8% of global GHG emissions in 2020 14 —more than aviation (1.9% 15 )—and, if unchecked, the ICT carbon footprint could grow exponentially in coming years 14 . Although the environmental impact of experimental ‘wet’ laboratories is more immediately obvious, with their large pieces of equipment and high plastic and reagent usage, the impact of algorithms is less clear and often underestimated. The risks of seeking performance at any cost and the importance of considering energy usage and sustainability when developing new hardware for high-performance computing (HPC) was raised as early as 2007 16 . Since then, continuous improvements have been made by developing new hardware, building lower-energy data centers and implementing more efficient HPC systems 17 , 18 . However, it is only in the past five years that these concerns have reached HPC users, in particular researchers. Notably, the field of artificial intelligence (AI) has first taken note of its environmental impacts, in particular those of the very large language models developed 19 , 20 , 21 , 22 , 23 . It is unclear, however, to what extent this has led the field towards more sustainable research practices. A small number of studies have also been performed in other fields, including bioinformatics 24 , astronomy and astrophysics 25 , 26 , 27 , 28 , particle physics 29 , neuroscience 30 and computational social sciences 31 . Health data science is starting to address the subject, but a recent systematic review found only 25 publications in the field over the past 12 years 32 . In addition to the environmental effects of electricity usage, manufacturing and disposal of hardware, there are also concerns around data centers’ water usage and land footprint 33 . Notably, computational science, in particular AI, has the potential to help fight climate change, for example, by improving the efficiency of wind farms, by facilitating low-carbon urban mobility and by better understanding and anticipating severe weather events 34 .

In this Perspective we highlight the nascent field of environmentally sustainable computational science (ESCS)—what we have learned from the research so far, and what scientists can do to mitigate their environmental impacts. In doing so, we present GREENER (Governance, Responsibility, Estimation, Energy and embodied impacts, New collaborations, Education and Research; Fig. 1 ), a set of principles for how the computational science community could lead the way in sustainable research practices, maximizing computational science’s benefit to both humanity and the environment.

The GREENER principles enable cultural change (blue arrows), which in turn facilitates their implementation (green arrows) and triggers a virtuous circle.

Environmental impacts of the computational sciences

The past three years have seen increased concerns regarding the carbon footprint of computations, and only recently have tools 21 , 35 , 36 , 37 and guidelines 38 been widely available to computational scientists to allow them to estimate their carbon footprint and be more environmentally sustainable.

Most calculators that estimate the carbon footprint of computations are targeted at machine learning tasks and so are primarily suited to Python pipelines, graphics processing units (GPUs) and/or cloud computing 36 , 37 , 39 , 40 . Python libraries have the benefit of integrating well into machine learning pipelines or online calculators for cloud GPUs 21 , 41 . Recently, a flexible online tool, the Green Algorithms calculator 35 , enabled the estimation of the carbon footprint for nearly any computational task, empowering sustainability metrics across fields, hardware, computing platforms and locations.

Some publications, such as ref. 38 , have listed simple actions that computational scientists can take regarding their environmental impact, including estimating the carbon footprint of running algorithms, both a posteriori to acknowledge the impact of a project and before starting as part of a cost–benefit analysis. A 2020 report from The Royal Society formalizes this with the notion of ‘energy proportionality’, meaning the environmental impacts of an innovation must be outweighed by its environmental or societal benefits 34 . It is also important to minimize electronic waste by keeping devices for longer and using second-hand hardware when possible. A 2021 report by the World Health Organization 42 warns of the dramatic effect of e-waste on population health, particularly children. The unregulated informal recycling industry, which handles more than 80% of the 53 million tonnes of e-waste, causes a high level of water, soil and air pollution, often in low- and middle-income countries 43 . The up to 56 million informal waste workers are also exposed to hazardous chemicals such as heavy metals and persistent organic pollutants 42 . Scientists can also choose energy-efficient hardware and computing facilities, while favoring those powered by green energy. Writing efficient code can substantially reduce the carbon footprint as well, and this can be done alongside making hardware requirements and carbon footprints clear when releasing new software. The Green Software Foundation ( https://greensoftware.foundation ) promotes carbon-aware coding to reduce the operational carbon footprint of the softwares used in all aspects of society. There is, however, a rebound effect to making algorithms and hardware more efficient: instead of reducing computing usage, increased efficiency encourages more analyses to be performed, which leads to a revaluation of the cost–benefit but often results in increased carbon footprints. The rebound effect is a key example of why research practice should adapt to technological advances so that they lead to carbon footprint reductions.

GREENER computational science

ESCS is an emerging field, but one that is of rapidly increasing importance given the climate crisis. In the following, our proposed set of principles (Fig. 1 ) outlines the main axes where progress is needed, where opportunities lie and where we believe efforts should be concentrated.

Governance and responsibility

Everyone involved in computational science has a role to play in making the field more sustainable, and many do already, from grassroots movements to large institutions. Individual and institutional responsibility is a necessary step to ensure transparency and reduction of GHG emission. Here we highlight key stakeholders alongside existing initiatives and future opportunities for involvement.

Grassroots initiatives led by graduate students, early career researchers and laboratory technicians have shown great success in tackling the carbon footprint of laboratory work, including Green Labs Netherlands 44 , the Nottingham Technical Sustainability Working Group or the Digital Humanities Climate Coalition 45 . International coalitions such as the Sustainable Research (SuRe) Symposium, initially set up for wet laboratories, have started to address the impact of computing as well. IT teams in HPC centers are naturally key, both in terms of training and ensuring that the appropriate information is logged so that scientists can follow the carbon footprints of their work. Principal investigators can encourage their teams to think about this issue and provide access to suitable training when needed.

Simultaneously, top–down approaches are needed, with funding bodies and journals occupying key positions in both incentivizing carbon-footprint reduction and in promoting transparency. Funding bodies can directly influence the researchers they fund and those applying for funding via their funding policies. They can require estimates of carbon footprints to be included in funding applications as part of ‘environmental impacts statements’. Many funding bodies include sustainability in their guidelines already; see, for example, the UK’s NIHR carbon reduction guidelines 1 , the brief mention of the environment in UKRI’s terms and conditions 46 , and the Wellcome Trust’s carbon-offsetting travel policy 47 .

Although these are important first steps, bolder action is needed to meet the urgency of climate change. For example, UKRI’s digital research infrastructure scoping project 48 , which seeks to provide a roadmap to net zero for its digital infrastructure, sends a clear message that sustainable research includes minimizing the GHG emissions from computation. The project not only raises awareness but will hopefully result in reductions in GHG emissions.

Large research institutes are key to managing and expanding centralized data infrastructures and trusted research environments (TREs). For example, EMBL’s European Bioinformatics Institute manages more than 40 data resources 49 , including AlphaFold DB 50 , which contains over 200,000,000 predicted protein structures that can be searched, browsed and retrieved according to the FAIR principles (findable, accessible, interoperable, reusable) 51 . As a consequence, researchers do not need to run the carbon-intensive AlphaFold algorithm for themselves and instead can just query the database. AlphaFold DB was queried programmatically over 700 million times and the web page was accessed 2.4 million times between August 2021 and October 2022. Institutions also have a role in making procurement decisions carefully, taking into account both the manufacturing and operational footprint of hardware purchases. This is critical, as the lifetime footprint of a computational facility is largely determined by the date it is purchased. Facilities could also better balance investment decisions, with a focus on attracting staff based on sustainable and efficient working environments, rather than high-powered hardware 52 .

However, increases in the efficiencies of digital technology alone are unlikely to prove sufficient in ensuring sustainable resource use 53 . Alongside these investments, funding bodies should support a shift towards more positive, inclusive and green research cultures, recognizing that more data or bigger models do not always translate into greater insights and that a ‘fit for purpose’ approach can ultimately be more efficient. Organizations such as Health Data Research UK and the UK Health Data Research Alliance have a key convening role in ensuring that awareness is raised around the climate impact of both infrastructure investment and computational methods.

Journals may incentivize authors to acknowledge and indeed estimate the carbon footprint of the work presented. Some authors already do this voluntarily (for example, refs. 54 , 55 , 56 , 57 , 58 , 59 ), mostly in bioinformatics and machine learning so far, but there is potential to expand it to other areas of computational science. In some instances, showing that a new tool is greener can be an argument in support of a new method 60 .

International societies in charge of organizing annual conferences may help scientists reduce the carbon footprint of presenting their work by offering hybrid options. The COVID-19 pandemic boosted virtual and hybrid meetings, which have a lower carbon footprint while increasing access and diversity 7 , 61 . Burtscher and colleagues found that running the annual meeting of the European Astronomical Society online emitted >3,000-fold less CO 2 e than the in-person meeting (0.582 tCO 2 e compared to 1,855 tCO 2 e) 25 . Institutions are starting to tackle this; for example, the University of Cambridge has released new travel guidelines encouraging virtual meetings whenever feasible and restricting flights to essential travel, while also acknowledging that different career stages have different needs 62 .

Industry partners will also need to be part of the discussion. Acknowledging and reducing computing environmental impact comes with added challenges in industry, such as shareholder interests and/or public relations. While the EU has backed some initiatives helping ICT-reliant companies to address their carbon footprint, such as ICTfootprint.eu, other major stakeholders have expressed skepticism regarding the environmental issues of machine learning models 63 , 64 . Although challenging, tech industry engagement and inclusion is nevertheless essential for tackling GHG emissions.

Estimate and report the energy consumption of algorithms

Estimating and monitoring the carbon footprint of computations is an essential step towards sustainable research as it identifies inefficiencies and opportunities for improvement. User-level metrics are crucial to understanding environmental impacts and promoting personal responsibility. In some HPC situations, particularly in academia, the financial cost of running computations is negligible and scientists may have the impression of unlimited and inconsequential computing capacity. Quantifying the carbon footprint of individual projects helps raise awareness of the true costs of research.

Although progress has been made in estimating energy usage and carbon footprints over the past few years, there are still barriers that prevent the routine estimation of environmental impacts. From task-agnostic, general-purpose calculators 35 and task-specific packages 36 , 37 , 65 to server-side softwares 66 , 67 , each estimation tool is a trade-off between ease of use and accuracy. A recent primer 68 discusses these different options in more detail and provides recommendations as to which approach fits a particular need.

Regardless of the calculator used, for these tools to work effectively and for scientists to have an accurate representation of their energy consumption, it is important to understand the power management for different components. For example, the power usage of processing cores such as central processing units (CPUs) and GPUs is not a readily available metric; instead, thermal design power (meaning, how much heat the chip can be expected to dissipate in a normal setting) is used. Although an acceptable approximation, it has also been shown to substantially underestimate power usage in some situations 69 . The efficiency of data centers is measured by the power usage effectiveness (PUE), which quantifies how much energy is needed for non-computing tasks, mainly cooling (efficient data centers have PUEs close to 1). This metric is widely used, with large cloud providers reporting low PUEs (for example, 1.11 for Google 70 compared to a global average of 1.57 71 ), but discrepancies in how it is calculated can limit PUE interpretation and thus its impact 72 , 73 , 74 . A standard from the International Organization for Standardization is trying to address this 75 . Unfortunately, the PUE of a particular data center, whether cloud or institutional, is rarely publicly documented. Thus, an important step is the data science and infrastructure community making both hardware and data centers’ energy consumption metrics available to their users and the public. Ultimately, tackling unnecessary carbon footprints will require transparency 34 .

Tackling energy and embodied impacts through new collaborations

Minimizing carbon intensity (meaning the carbon footprint of producing electricity) is one of the most immediately impactful ways to reduce GHG emissions. Carbon intensities depend largely on geographical location, with up to three orders of magnitude between the top and bottom performing high-income countries in terms of low carbon energies (from 0.10 gCO 2 e kWh −1 in Iceland to 770 gCO 2 e kWh −1 in Australia 76 ). Changing the carbon intensity of a local state or national government is nearly always impractical as it would necessitate protracted campaigns to change energy policies. An alternative is to relocate computations to low-carbon settings and countries, but, depending on the type of facility or the sensitivity of the data, this may not always be possible. New inter-institutional cooperation may open up opportunities to enable access to low-carbon data centers in real time.

It is, however, essential to recognize and account for inequalities between countries in terms of access to green energy sources. International cooperation is key to providing scientists from low- and middle-income countries (LMICs), who frequently only have high-carbon-intensity options available to them, access to low-carbon computing infrastructures for their work. In the longer term, international partnerships between organizations and nations can help build low-carbon computing capacity in LMICs.

Furthermore, the footprint of user devices should not be forgotten. In one estimate, the energy footprint of streaming a video to a laptop is mainly on the laptop (72%), with 23% used in transmission and a mere 5% at the data center 77 . Zero clients (user devices with no compute or storage capacity) can be used in some research use cases and drastically reduce the client-side footprint 78 .

It can be tempting to reduce the environmental impacts of computing to electricity needs, as these are the easiest ones to estimate. However, water usage, ecological impacts and embodied carbon footprints from manufacturing should also be addressed. For example, for personal hardware, such as laptops, 70–80% of the life-cycle impact of these devices comes from manufacturing only 79 , as it involves mining raw materials and assembling the different components, which require water and energy. Moreover, manufacturing often takes place in countries that have a higher carbon intensity for power generation and a slower transition to zero-carbon power 80 . Currently, hardware renewal policies, either for work computers or servers in data centers, are often closely dependent on warranties and financial costs, with environmental costs rarely considered. For hardware used in data centers, regular updates may be both financially and environmentally friendly, as efficiency gains may offset manufacturing impacts. Estimating these environmental impacts will allow HPC teams to know for sure. Reconditioned and remanufactured laptops and servers are available, but growth of this sector is currently limited by negative consumer perception 81 . Major suppliers of hardware are making substantial commitments, such as 100% renewable energy supply by 2030 82 or net zero by 2050 83 .

Another key consideration is data storage. Scientific datasets are now measured in petabytes (PB). In genomics, the popular UK Biobank cohort 84 is expected to reach 15 PB by 2025 85 , and the first image of a black hole required the collection of 5 PB of data 86 . The carbon footprint of storing data depends on numerous factors, but based on some manufacturers’ estimations, the order of magnitude of the life-cycle footprint of storing 1 TB of data for a year is ~10 kg CO 2 e (refs. 87 , 88 ). This issue is exacerbated by the duplication of such datasets in order for each institution, and sometimes each research group, to have a copy. Centralized and collaborative computing resources (such as TREs) holding both data and computing hardware may help alleviate redundant resources. TRE efforts in the UK span both health (for example, NHS Digital 89 ) and administrative data (for example, the SAIL databank on the UK Secure Research Platform 90 and the Office for National Statistics Secure Research Service 91 ). Large (hyperscale) data centers are expected to be more energy-efficient 92 , but they may also encourage unnecessary increases in the scale of computing (rebound effect).

The importance of dedicated education and research efforts for ESCS

Education is essential to raise awareness with different stakeholders. In lieu of incorporating some aspects into more formal undergraduate programs, integrating sustainability into computational training courses is a tangible first step toward reducing carbon footprints. An example is the ‘Green Computing’ Workshop on Education at the 2022 conference on Intelligent Systems for Molecular Biology.

Investing in research that will catalyze innovation in the field of ESCS is a crucial role for funders and institutions to play. Although global data centers’ workloads have increased more than sixfold between 2010 and 2018, their total electricity usage has been approximately stable due to the use of power-efficient hardware 93 , but environmentally sustainable investments will be needed to perpetuate this trend. Initiatives like Wellcome’s Research Sustainability project 94 , which look to highlight key gaps where investment could deliver the next generation of ESCS tools and technology, are key to ensuring that growth in energy demand beyond current efficiency trends can be managed in a sustainable way. Similarly, the UKRI Data and Analytics Research Environments UK program (DARE UK) needs to ensure that sustainability is a key evaluation criterion for funding and infrastructure investments for the next generation of TREs.

Recent studies found that the most widely used programming languages in research, such as R and Python 95 , tend to be the least energy-efficient ones 96 , 97 , and, although it is unlikely that forcing the community to switch to more efficient languages would benefit the environment in the short term (due to inefficient coding for example), this highlights the importance of having trained research software engineers within research groups to ensure that the algorithms used are efficiently implemented. There is also scope to use current tools more efficiently by better understanding and monitoring how coding choices impact carbon footprints. Algorithms also come with high memory requirements, sometimes using more energy than processors 98 . Unfortunately, memory power usage remains poorly optimized, as speed of access is almost always favored over energy efficiency 99 . Providing users and software engineers with the flexibility to opt for energy efficiency would present an opportunity for a reduction in GHG emissions 100 , 101 .

Cultural change

In parallel to the technological reductions in energy usage and carbon footprints, research practices will also need to change to avoid rebound effects 38 . Similar to the aviation industry, there is a tendency to count on technology to solve sustainability concerns without having to change usage 102 (that is, waiting on computing to become zero-carbon rather than acting on how we use it). Cultural change in the computing community to reconsider how we think about computing costs will be necessary. Research strategies at all levels will need to consider environmental impacts and corresponding approaches to carbon footprint minimization. The upcoming extension of the LEAF standard for computational laboratories will provide researchers with tangible tools to do so. Day to day, there is a need to solve trade-offs between the speed of computation, accuracy and GHG emissions, keeping in mind the goal of GHG reduction. These changes in scientific practices are challenging, but, importantly, there are synergies between open computational science and green computing 103 . For example, making code, data and models FAIR so that other scientists avoid unnecessary computations can increase the reach and impact of a project. FAIR practices can result in highly efficient code implementations, reduce the need to retrain models, and reduce unnecessary data generation/storage, thus reducing the overall carbon footprint. As a result, green computing and FAIR practices may both stimulate innovation and reduce financial costs.

Moreover, computational science has downstream effects on carbon footprints in other areas. In the biomedical sciences, developments in machine learning and computer vision impact the speed and scale of medical imaging processing. Discoveries in health data science make their way to clinicians and patients through, for example, connected devices. In each of these cases and many others, environmental impacts propagate through the whole digital health sector 32 . Yet, here too synergies exist. In many cases, such as telemedicine, there may be a net benefit in terms of both carbon and patient care, provided that all impacts have been carefully accounted for. These questions are beginning to be tackled in medicine, such as assessments of the environmental impact of telehealth 104 or studies into ways to sustainably handle large volumes of medical imaging data 105 . For the latter, NHS Digital (the UK’s national provider of information, data and IT systems for health and social care) has released guidelines to this effect 106 . Outside the biomedical field, there are immense but, so far, unrealized opportunities for similar efforts.

The computational sciences have an opportunity to lead the way in sustainability, which may be achieved through the GREENER principles for ESCS (Fig. 1 ): Governance, Responsibility, Estimation, Energy and embodied impacts, New collaborations, Education and Research. This will require more transparency on environmental impacts. Although some tools already exist to estimate carbon footprints, more specialized ones will be needed alongside a clearer understanding of the carbon footprint of hardware and facilities, as well as more systematic monitoring and acknowledgment of carbon footprints. Measurement is a first step, followed by a reduction in GHG emissions. This can be achieved with better training and sensible policies for renewing hardware and storing data. Cooperation, open science and equitable access to low-carbon computing facilities will also be crucial 107 . Computing practices will need to adapt to include carbon footprints in cost–benefit analyses, as well as consider the environmental impacts of downstream applications. The development of sustainable solutions will need particularly careful consideration, as they frequently have the least benefit for populations, often in LMICs, who suffer the most from climate change 22 , 108 . All stakeholders have a role to play, from funding bodies, journals and institutions to HPC teams and early career researchers. There is now a window of time and an immense opportunity to transform computational science into an exemplar of broad societal impact and sustainability.

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Acknowledgements

L.L. was supported by the University of Cambridge MRC DTP (MR/S502443/1) and the BHF program grant (RG/18/13/33946). M.I. was supported by the Munz Chair of Cardiovascular Prediction and Prevention and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312). M.I. was also supported by the UK Economic and Social Research 878 Council (ES/T013192/1). This work was supported by core funding from the British Heart Foundation (RG/13/13/30194; RG/18/13/33946) and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was also supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland) and the British Heart Foundation and Wellcome.

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Lannelongue, L., Aronson, HE.G., Bateman, A. et al. GREENER principles for environmentally sustainable computational science. Nat Comput Sci 3 , 514–521 (2023). https://doi.org/10.1038/s43588-023-00461-y

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Title: on the opportunities of green computing: a survey.

Abstract: Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades, and is widely used in many areas including computing vision, natural language processing, time-series analysis, speech synthesis, etc. During the age of deep learning, especially with the arise of Large Language Models, a large majority of researchers' attention is paid on pursuing new state-of-the-art (SOTA) results, resulting in ever increasing of model size and computational complexity. The needs for high computing power brings higher carbon emission and undermines research fairness by preventing small or medium-sized research institutions and companies with limited funding in participating in research. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic. In this survey, we give a systematic overview of the technologies used in Green Computing. We propose the framework of Green Computing and devide it into four key components: (1) Measures of Greenness, (2) Energy-Efficient AI, (3) Energy-Efficient Computing Systems and (4) AI Use Cases for Sustainability. For each components, we discuss the research progress made and the commonly used techniques to optimize the AI efficiency. We conclude that this new research direction has the potential to address the conflicts between resource constraints and AI development. We encourage more researchers to put attention on this direction and make AI more environmental friendly.

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Please note you do not have access to teaching notes, green computing for sustainable future technologies and its applications.

Contemporary Studies of Risks in Emerging Technology, Part A

ISBN : 978-1-80455-563-7 , eISBN : 978-1-80455-562-0

Publication date: 10 May 2023

Purpose: Green computing is a way of using the computer resource in an eco-friendly while maintaining and decreasing the harmful environmental impact. Minimising toxic materials and reducing energy usage can also be used to recycle the product.

Need for the Study: The motivation of the study is to use green computing resources to decrease carbon emissions and their adverse effect on the environment.

Methodology: The study uses a qualitative method of collecting resources and data to address the opportunities, challenges, and future trends in green computing for Sustainable Future Technologies. The study focusses on multiple kinds of cloud computing services collected and executed into single remote servers. The service demand processor offers these services to the client per their needs. The simultaneous requests to access the cloud services, processing and expertly managing these requests by the processors are discussed and analysed.

Findings: The findings suggest that green computing is an upcoming and most promising area. The number of resources employed for green computing can be beneficial for lowering E-waste so that computing can be environmentally friendly and self-sustainable.

Practical Implications: Green computing applies across all industries and service sectors like healthcare, entertainment, tourism, and education. The convergence of technologies like Cloud Computing, AI, and Internet of Things (IoT) is greatly impacting Green Supply Chain Management (GSCM) market.

• Green computing
• Sustainable development
• Data centre
• Energy efficiency
• Green energy

Ahmad, S. , Mishra, S. and Sharma, V. (2023), "Green Computing for Sustainable Future Technologies and Its Applications", Grima, S. , Sood, K. and Özen, E. (Ed.) Contemporary Studies of Risks in Emerging Technology, Part A ( Emerald Studies in Finance, Insurance, and Risk Management ), Emerald Publishing Limited, Leeds, pp. 241-256. https://doi.org/10.1108/978-1-80455-562-020231016

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Copyright © 2023 Shazib Ahmad, Saksham Mishra and Vandana Sharma

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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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• Published: 18 July 2017

Greening emerging IT technologies: techniques and practices

• Junaid Shuja 1 ,
• Raja Wasim Ahmad 1 ,
• Abdullah Gani 2 ,
• Abdelmuttlib Ibrahim Abdalla Ahmed 2 ,
• Aisha Siddiqa 2 ,
• Kashif Nisar 3 ,
• Samee U. Khan 4 &
• Albert Y. Zomaya 5  

Journal of Internet Services and Applications volume  8 , Article number:  9 ( 2017 ) Cite this article

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The tremendous increase in global industrial activity has resulted in high utilization of natural energy resources and increase in global warming over the last few decades. Meanwhile, computing has become a popular utility of modern human lifestyle. With the increased popularity of computing and IT services, the corresponding energy consumption of the IT industry has also increased rapidly. The computing community realizes the importance of green measures and provides technological solutions that lead to its energy-aware operations along with facilitating the same in other IT enabled industries. Green and sustainable computing practices review the environmental impact of the computing industry to encourage the adoption of practices and technologies for efficient operations. “Green Computing” paradigm advocates the energy-proportional and efficient usage of computing resources in all emerging technologies, such as Big Data and Internet of Things (IoT). This article presents a review of green computing techniques amidst the emerging IT technologies that are evident in our society. The best practices for green computing and the trade-off between green and high-performance policies is debated. Further, we discuss the imminent challenges facing the efficient green operations of emerging IT technologies.

1 Introduction

Global industrial growth has had two demanding effects on the human environment. First, natural energy resources are being consumed at a rapid pace. Efficient operations and alternate energy resources are sought to reduce the current rate of depletion of natural energy resources. Second, global industrial growth has resulted in increased carbon emissions. The carbon emissions, known as Greenhouse Gases (GHG) in general, lead to higher disease rates, global warming, and depletion of the Ozone layer. Information Technology (IT) is both an emerging global industry and a support technology for many businesses. We seek information at increasing rates and in multiple forms to ease our lifestyle. The IT industry, or computing in general, contributes to both high energy consumption and carbon emissions. Therefore, emerging IT technologies, existing practices, and algorithms need to be redefined for energy efficient, energy-proportional, and sustainable operations. Additionally, IT technologies have a responsibility to limit the energy consumption and carbon footprint of other industries and organizations while facilitating green environmental practices in their daily operations [ 1 ].

Many modern aspects of our society are based on the global success of the IT industry. The problem of energy and sustainability is often associated with manufacturing, aviation, and petroleum industries. However, the IT sector is also accountable for high energy consumption and carbon emissions. The IT sector is currently responsible for 2.4-3% of global electricity consumption with aforecasted 20% increase annually. Similarly, the IT sector accounts for 2-2.5% of worldwide carbon emissions equivalent to 0.86 metric gigatonnes of C O 2 [ 2 ]. The increasing energy and carbon impact of computing call for energy-proportional and “Green” computing systems.

Green Computing is a computing paradigm where: (a) IT resource efficiencies are maximized, (b) resources (in particular, energy) are re-used whenever possible, (c) sustainable products and manufacturing practices are adopted, and (d) green initiatives in other industries are supported through monitoring and management tools [ 3 ]. Resource efficiency has dual context for performance and energy. Multiple resources and their alternative backups are utilized for efficient high-performance computing. In contrast, energy efficiency practices involve reduction of resources and energy proportional computing. Energy re-use in computing systems derives from the cyber-physical interactions of IT resources and cooling of large-scale IT centers [ 1 ]. Similarly, sustainability in computing is achieved by utilization of renewable energy resources while limiting the carbon footprint of IT operations. Manufacturing practices that increase re-use of off-the-shelf computing components and limit the e-waste also contribute to sustainability efforts. Other than self-conscious approaches to green computing, the IT technologies are utilized as a platform to promote the greening and sustainability efforts of other industries through environmental monitoring and social awareness [ 4 ].

In recent years, multiple IT technologies have integrated into people’s lifestyles seamlessly while facilitating day-to-day tasks, such as social communications, healthcare monitoring, and environmental management [ 5 ]. We present the “Green Computing” paradigm in this article from the perspective of emerging IT technologies and their green initiatives. We select (a) cloud computing, (b) mobile computing, (c) Internet of Things (IoT), (d) big data analytics, and (d) software-based networks as emerging IT technologies. This article is different from previous efforts on summarizing the green technologies in segregated IT technologies. The contributions of this article are (a) we provide a survey on green algorithms, circuits, architectures, and practices in emerging IT technologies of this decade, (b) we highlight the key requirements and practices for the greening of emerging IT technologies, and (c) we emphasize future research trends in the field of green computing.

Multiple computing technologies have emerged over the last decade as enablers of scientific, industrial, and social businesses. To select the emerging IT technologies for a discussion on green computing, we set metrics on popularity, social integration, and future application in smart environments [ 6 ]. We chose cloud computing, mobile computing, IoT, big data analytics, and software-based networks as the emerging IT technologies based on the aforementioned metrics. The integration and high correlation of these technologies create opportunities that assist various organizations in performing their duties efficiently. For instance, due to high integration of the technologies, law enforcement agencies were able to detect the bomber behind the Boston marathon bombing in four days. During the investigation, the FBI utilized cloud computing resources to process the large amount of data collected through edge-based devices (smartphones, cameras, and sensors) [ 7 ]. International Data Corporation (IDC) annual reports also point to the increasing revenue and usage of cloud computing, mobile computing, IoT, big data analytics and software-based networks technologies [ 8 , 9 ].

The rest of the article is structured as follows. Sections 2 , 3 , 4 , 5 , and 6 discuss the greening of cloud computing, mobile computing, IoT, Big data analytics, and software-based networks respectively. In Section 7 , research issues and challenges to the green computing paradigm and emerging IT technologies are listed. We provide concluding remarks in Section 8 .

2 Green cloud computing

Cloud computing has established itself as an enabling technology for multiple IT services. The increase in the number of cloud-based IT services and applications demands establishment of data centers that house thousands of web servers, storage, and network devices. Cloud data centers (CDC) provide a range of services from high-performance computing to large-scale data analytics to end users. The massive scale of cloud data centers that are setup at multiple geographical locations to facilitate distributed users means that they contribute 25% to the total IT electricity share [ 10 ]. Moreover, IT services are shifting from single server operations to rack-mounted blade servers. The rack-mounted server designs result in higher electronic densities, higher energy consumption, and heat dissipation [ 11 ]. As a result, both direct energy and indirect cooling energy demands rise in cloud data centers. The techniques to “green” cloud data center operations can be broadly classified into three categories: (a) resource management with virtualization, (b) sustainability with renewable energy and waste heat utilization, (c) and resource scheduling with state-of-the-art evolutionary algorithms [ 12 ].

Cloud data center resources are managed by a virtualization layer that resides over the physical resources. The virtualization layer abstracts the hardware layer interfaces to provide a higher level interface for users and applications. The virtualization layer helps in management and consolidation of cloud data center resources through multiple backup techniques, such as resource migration and snapshot [ 13 ]. The primary objective of virtualization in cloud data centers is to provide scalable and fault-tolerant operations. Increasingly, virtualization is being used for resource consolidation and energy efficiency. A virtual resource residing on a 40% utilized server can be migrated to another 40% utilized server while the former is operated in low-power idle mode [ 10 ]. The virtual machine (VM) migration is exploited in both inter and intra-data center configurations while providing energy efficient operations. However, the network cost resulting from the VM migration needs to be addressed for joint network and server resource optimization [ 14 ]. The intra-data center VM migration network cost is reduced by placing related and “talkative” VMs in optimal server proximity so that their communications are limited to a part of the network [ 15 ]. Similarly, the inter-data center VM migration cost is reduced by data deduplication and compression techniques over long-haul networks [ 16 ].

The green computing initiative also embodies sustainability in operations. Cloud data centers operating on renewable energy resources lead to zero GHG emissions. Renewable energy from sources such as the sun and the wind can be generated from on-site installations or purchased from off-site corporations [ 4 ]. The main drawbacks of renewable energy based cloud data center operations are the associated cost and unpredictable supply of the renewable resource. It is estimated that with the advances in storage capacities, the cost/Watt of renewable energy will halve in the next decade [ 17 ]. Moreover, to address the unpredictability of renewable energy resources, techniques such as dynamic power-workload balancing and server power capping are exploited [ 1 ]. The integration of renewable energy resources to cloud data center power designs requires utilization of hybrid power supplies and Autonomic Transfer Switches (ATS). The ATS shift power between grid and renewable energy resources to match the dynamic data center workload with the power generation [ 18 ].

The re-use of resources is a major goal of the green computing paradigm. Modern modular data centers with blade servers of higher electronic densities are leading to increased cooling requirements. It is estimated that 40% of data center electricity is used in cooling the servers while keeping their temperatures in operational range [ 19 ]. The waste heat generated by data centers can be utilized or re-used in various waste heat recovery scenarios. Firstly, cloud data centers provide ample opportunity for waste heat re-use in the cooling process. The heat recovered from servers is captured in the vapor-absorption based cooling systems where reversible heat pumps transfer thermal energy to cooler space. Secondly, in cooler places, data centers can be co-located with residential buildings for district heating [ 20 ]. Thirdly, modular data center designs can be migrated to cooler areas to reduce the cooling requirements while directly utilizing ambient air in the cooling process [ 1 ]. The major concern with energy re-use in CDCs is the low quality of heat generated that is applicable to few heat recovery processes [ 11 ].

The basic objective of cloud data centers is to provide IT services at an optimal pay-as-you-go model. In most software-based green cloud computing solutions, the network, processor, storage devices, and user tasks are modeled as graph and tree-based structures. The resultant optimization model of the cloud services generally focuses on the task makespan and cost minimization while determining which task is allocated to which resource [ 21 ]. Recently, with a shift in focus on the energy consumption, the energy costs have been included in the optimization models. However, the task makespan and energy minimization requirements often conflict. The task makespan minimization requires exploitation of multiple resources that leads to higher energy costs. The multi-objective modeling of the task allocation problem in cloud data centers with thousands of resources and applications leads to great complexity in solution finding [ 22 ]. Evolutionary algorithms are employed to swiftly find near-optimal solutions for the multi-objective energy efficient resource scheduling problems in cloud data centers [ 3 ]. Interested readers can refer to comprehensive surveys on the greening of cloud computing [ 1 , 21 ]. Figure 1 illustrates the options for greening cloud computing systems.

Green Cloud Computing Techniques

3 Green mobile computing

Smartphones of recent generations are equipped with high storage capacity and the computational power to perform resource-intensive tasks. The preference of smartphone users has lessened the dependency on desktop servers to perform computing tasks. As a result, the resource requirements of the smartphone applications have also increased [ 23 ]. Emerging media-rich smartphone applications frequently trigger sensors, such as GPS, accelerometer, and wireless radios to provide context-aware services. As a result, the computation, communication, and energy cost of smartphones significantly increase. To handle the energy-performance trade-off, energy-efficient system designs are necessary to meet the requirements of modern smartphone devices. Moreover, the energy-efficient design of smartphone applications and system components. Energy estimation helps to identify the rogue applications within a smartphone [ 24 ].

Effective management of the hardware components of a smartphone device significantly improves the total energy budget. The architectural design of hardware modules within the smartphone is based on Complementary Metal–Oxide–Semiconductor (CMOS). The total power consumption of CMOS based circuits (e.g. CPU, static RAM, and GPU) consists of static and dynamic power. The static power of a circuit varies from device to device depending on the insulation capabilities of transistors and represents the power consumption when the transistor is not in the switching state [ 25 ]. Dynamic power represents the power consumption when a device changes logic state from on to off or vice versa. Power gating embeds a high voltage threshold transistor between actual ground and circuit ground of a device to switch off the transistor during its sleep hours to reduce leakage power. For the CPU module, dynamic frequency scaling (DFS) enables dynamic adjustment of power consumption for greening the smartphones at the cost of throughput [ 26 ]. The tail power represents the state of a smartphone component that remains in high power state although it has already finished its required task. The tail power state of smartphone components such as Wi-Fi, 3G, GPS, and SD-CARD, depletes battery charge quickly. Software tools, such as E-prof, empower smartphones to measure/estimate the device energy consumption at the component level. However, software-based solutions significantly impact the device’s energy consumption due to their profiling activities [ 27 ].

Software based green computing solutions such as mobile cloud computing based computational offloading, energy bug handling, and energy efficient application development significantly reduce the energy budget of the smartphone. Mobile cloud computing empowers smartphone devices to augment device lifetime by carefully offloading energy critical tasks to remote cloud servers. Computational offloading decisions consider total execution time, resource consumption, energy requirements, and privacy issues of an application before migrating a task to resource-rich cloud servers [ 28 ]. Energy bugs within a smartphone lead to abnormal power consumption behavior of mobile applications. Energy bugs are difficult to track, and mainly occur due to (a) faulty batteries, (b) damaged mobile battery chargers, (c) infected memory cards, and (d) damaged SIM cards. Alternatively, within an OS, changing OS configuration impacts the mobile battery power consumption rate. For instance, setting SetCPU function incorrectly for kernel overclocking results in high battery power consumption [ 29 ]. Similarly, infected mobile applications and frameworks also drain mobile application abnormally. For instance, a “no sleep” bug hinders a smartphone component from going into a sleep state that consequently depletes mobile battery charge. A mobile application, with no sleep bug, acquires a lock on a mobile component and does not release it for a long period of time. The ADEL framework reported energy bugs of Wi-Fi components by tracking the packet transmission rate within the mobile application using dynamic taint-tracking analysis. Handling energy bugs puts some extra burden on programmers to explicitly manipulate power control APIs for energy-efficient operations of mobile applications [ 24 ].

Smartphone energy estimation provides the basis for green computing within smartphones. It provides feedback to the application developers to consider energy as a metric in addition to maintainability, complexity, and understandability. Smartphone application energy estimation schemes are broadly classified into components power model and code analysis based estimation categories. Component power model based methods use State of Charge (SOC) estimation methods to forecast the energy consumption of an application [ 24 , 30 ]. Alternatively, the code analysis based method considers base cost energy of instructions within the source code of an application to estimate energy consumption [ 31 ]. SOC estimation methods include coulomb counting and voltage based methods. Coulomb counting estimates SOC by communicating to the smartphone’s built-in sensors to find the accumulative current drop rate over time. However, coulomb counting produces inaccurate estimation results due to internal factors such as battery aging, the temperature within the smartphone, and charging/discharging rate. Alternatively, voltage-based SOC estimation employs fuel gauge sensors. Fuel gauge sensors are inaccurate owing to low charge update rate. Base cost energy methods assign base cost to the operations within an instruction to estimate energy consumption of an application based on static code analysis. The estimation method helps either to improve the hardware components of smartphones or software for green computing [ 31 ]. Figure 2 highlights the hardware and software options for green mobile computing.

Green Mobile Computing. The figure depicts techniques for green mobile computing

Inefficient code design within a smartphone application has a high impact on the total energy consumption. Within an application, resource optimal placement of classes and functions reduces the power consumption. For instance, minimizing the memory distance between two functions that frequently communicate reduces the energy consumption of target application [ 32 ]. Also, educating developers with energy efficient application development techniques including loop unrolling, branch optimization, dead store elimination, value numbering, code inlining, constant propagation, code motion, inter-procedural analysis, and instruction scheduling, greens smartphone operations [ 33 ]. Extensive studies on green mobile computing are listed for detailed analysis [ 24 , 34 ].

4 Green internet of things

IoT is another emerging technology that facilitates data communication among multiple electronic devices without human and computer intervention. Green IoT is a set of procedures adopted by the IoT in the form of hardware or software efficiency techniques. Green IoT aims to achieve energy efficiency through the reduction of the greenhouse effect in the current services and applications. Moreover, to reduce the impact on the environment, Green IoT focuses on the issues of green productions, green redesign, and green recycling/disposal [ 35 ]. Table 1 highlights enabling technologies and greening strategies for IoT.

Real deployment of IoT is performed through the collaboration of enabling technologies, communication strategies, and protocols. This section mainly focuses on the most crucial communication strategies and technologies that lead towards green IoT.

Green Radio-Frequency IDentification (GRFID) Radio Frequency Identification (RFID) is one of the promising IoT enablers. A RFID system comprises of RFID tags and tags readers. RFID tags are in the form of microchips attached to the radio that works as a transceiver. Every RFID tag has a unique ID and can store context data regarding the entities to which they are attached. Generally, in the elementary process, the RFID tag reader triggers information flow through transmitting a query signal. Consequently, the responses come from the nearby RFID tags. Mostly, RFID system transmission ranges are not more than a few meters. Moreover, the transmission frequencies start at 124-135 kHz up to ultra-high at 860-960 MHz. Currently, RFID tags can be found in two types: active and passive tags. The active tag uses onboard power batteries to do its functions. The passive tags depend on harvesting energy from the signal of the readers following the principle of induction [ 36 ].

To obtain green RFID, two factors should be considered. Firstly, RFID tag sizes should be reduced since tags themselves are difficult to recycle. Consequently, the amount of non-degradable material should be reduced in tag manufacturing (e.g. printable RFID tags, paper-based RFID tags, and biodegradable RFID tags). Secondly, using communication algorithms and protocols that support energy efficiency can lead to Green IoT. Green communication protocols provide energy efficiency through dynamic adjustment of the level of transmission power, optimization of tag estimation, and avoiding of tag collision and overhearing [ 37 ].

Green Wireless Sensor Network (GWSN) A Wireless Sensor Network (WSN) comprise of numerous sensor nodes that have resource-constraints, such as limited computing capability, storage capacity, and power. Commonly, the sensor nodes are connected to a powerful base station called sink. Usually, sensor nodes are equipped with multiple on-board sensors to read the surroundings circumstances, such as humidity, temperature, acceleration, etc. Commercial WSN solutions are based on the IEEE 802.15.4 standard [ 38 ]. Techniques such as (a) sleep mode activation during sensor idle time, (b) wireless charging mechanisms that harvest environmental mechanisms, (c) radio optimization, and (d) energy efficient routing and data collection are utilized for GWSN [ 39 ].

WSN aggregate sensed data into a sink from cluster heads through event-detection and continuous monitoring. Cluster heads receive and send aggregated data continuously, which leads to faster energy depletion around the sink [ 40 ]. There are two strategies for optimizing energy usage in WSN, namely, periodic reporting instead of continuous monitoring and timestamp-less synchronization. In a periodic reporting strategy, time periods of data reporting are set by the sensor owner to avoid energy spikes that are raised in event-driven reporting. In timestamp-less synchronization, the broadcast control messages to sensors for synchronization are not put to practice. The participating sensor pairs performs REQUEST/RESPONSE until the achievement of synchronization process [ 41 ].

Green M2M communication(GM2MC) Machine-to-machine (M2M) communication is one of the popular paradigms in IoT. There are two communication domains in IoT: M2M and networks. In an M2M domain, multiple nodes are deployed to intelligently monitor and gather data. In the network domain, wireless/wired networks carry the gathered data to the desired base station (BS). The BS supports different M2M applications through the network. The challenge is that the massive nodes involved in M2M interactions consume a lot of energy. The techniques that can be utilized to improve energy-efficiency of M2M communications are: (a) intelligently adjusting the transmission power to the necessary level, (b) developing energy-efficient routing protocols, (c) scheduling the activity in the machine domain, and (d) using energy-harvesting techniques [ 42 ]. Zhu et al. [ 39 ] provide exhaustive reading on Green IoT technologies.

5 Green big data analytics

Big data introduces the era of data with new challenges such as petabyte scale structured and unstructured data sets which are growing at an exponential rate and have heterogeneous formats. Fast data retrieval and accuracy of search from a pool of big data are the main challenges to maximize value for decision making in big data analytics [ 43 ]. Traditional data management systems lack the capability to handle big data storage and analytics requirements and thus NoSQL technology is contributing to provide suitable solutions for timely data retrieval and efficient data processing. The process of greening is crucial for big data as analytics on tremendous size of data sets requires high computing power, scalable and efficient storage space, high availability of main memory, and fast communication media on always-on local physical or enterprise cloud servers [ 44 ]. Consequently, green big data analytics requires efficiency in resource utilization, energy consumption, and infrastructure scalability.

Big data analytics procedures may contribute to preserving the usage of processing and storage resources, scalability of systems, and improved productivity. Big data analytics requirements such as high availability, reliability, and consistency are significant in the development of technological infrastructures. However, energy preserving and resource optimization are the green computing aspects of analytics which have not being reported in the literature frequently. Cloud computing is revealed as a big data analytics technology which offers resource outsourcing in order to avoid physical occupation and thus multiple users with varying analytics requirements can utilize remotely accessible resources. The advancement in cloud computing for big data analytics is expected to lead to low dependency on the usage of personal computers in the new era of computing. Along with resource preservation, cloud computing also offers lower energy consumption for executing high computational procedures on big data [ 45 ]. Cloud computing has great importance as being a highly available platform for big data analytics which allows minimization in resource utilization and energy consumption [ 46 ].

There is a visible advancement in today’s technology towards green big data analytics. For instance, GreenPlum [ 47 ] and GreenHadoop [ 48 ] are proposed in big data analytics for green computing. GreenPlum is an open source data warehouse, licensed under Apache Inc., which offers fast analytics on petabyte-scale data with efficient query processing via parallel processing and optimization. Cost-based query optimization introduced by GreenPlum ensures high analytics on large volume data sets with usage efficiency. GreenHadoop, on the other hand, brings the idea of renewable energy sources in order to balance the supply and demand of energy sources associated with big data analytics. The GreenHadoop framework uses a photovoltaic solar array and electrical grid energy resources. The GreenHadoop framework for green analytics achieves maximized energy consumption by estimating available solar energy and scheduling MapReduce jobs accordingly. GreenPlum provides support to both batch and interactive modes of processing. However, GreenHadoop achieves real-time energy estimates based on prior data center workload.

Figure 3 shows a green big data analytics process where storage and processing resources reside on clouds and can be requested on demand. Cloud computing technology provides the basis for green big data analytics as the optimum resource utilization with reduced energy consumption. Currently, major big data sources and consumers are social networks, healthcare, industries, commerce, and business enterprises. Data from these sources and consumers is extensively scalable and brings critical analytics requirements for timely decision making. This big data storage and processing load are efficiently handled by data centers and processors residing on the cloud which ensures green analytics. According to a study [ 49 ], it is estimated that cloud computing will be able to achieve 38% reduction in energy usage by 2020. The concept of recycling is stated in [ 50 ] which suggests that renewable energy technology will be a preferable choice of investment in finding energy resources by 2040. Renewable energy technology is emerging with reduced adaptation cost, efficient green housing, and increased renewability demands which aim to achieve reduced carbon discharge, lower and stable energy costs, and access to reliable energy sources. 64% of the IT industry are meeting their targets of energy saving by using renewable energy technology [ 51 ].

Green Big Data Analytics. The figure depicts techniques for green big data analytics

Green big data analytics is significant in optimizing energy consumption and re-usability of available sources to meet extensive analytics requirements of big data. Green computing is analogous to green chemistry and allows usage minimization for enormous computing and storage resources required by big data. Green computing aligns the big data analytics technologies with the concept of sustainability i.e. reduction, reusability, and recycling. Researchers [ 51 ] suggest that the technology industry seems more concerned about analytics efficiency than environmental sustainability and computational complexity. However, implementation of green analytics on big data surely results in reduced memory usage and computational cost. Interested readers can refer to an extensive future perspective on green big data analytics [ 52 , 53 ].

6 Green networking

Networks are the basic component and enabler of the innovations that have occurred in human society in the past few decades. As more industries and business have integrated IT technologies and services, the networks have grown into complex structures connecting billions of devices worldwide. As a result, network devices consume a large amount of energy constituting approximately 10% of the aggregate IT energy consumption [ 54 ]. The basic techniques applied for energy efficient networks are: (a) energy efficient protocols for routing, medium access, hand-off and (b) Adaptive Link Rate (ALR) techniques that scale link rate and utilize sleep states for energy-proportional computing [ 55 ].

The software and virtualization techniques have led to current advancements in the energy efficiency of networking technologies. Software Defined Networks (SDN) separate the data and control plane of network routers with the help of a central controller. SDN do not have a direct impact on the energy consumption of a network. However, the pervasive programmable interface of SDN supports energy efficient network operations indirectly through resource consolidation [ 56 ]. A minimum energy efficient subset of network resources can be calculated through a resource optimization technique and implemented through SDN as demonstrated in [ 57 ]. Hence, server and network resource management techniques can be utilized in parallel with the virtualization and SDN enabling technologies. SDN can help implement green computing policies at the network level based on their programmable control plane. Similarly, security policies can be implemented with the help of SDN while eliminating the need for stand-alone security devices. Consequently, SDN-enabled network devices can also implement security functions, lowering the total operational costs and energy bill [ 58 ].

Network Function Virtualization (NFV) is another technological shift in telecommunication systems. NFV decouples network forwarding and routing functions from underlying physical systems through virtualization [ 59 ]. Network functions, such as a firewall, can be implemented in software (virtual network function) and implemented on any of the industry standard physical servers. Similarly, network devices can offer virtual computation services. As a result of virtualization, network and compute devices offer agile computing and forwarding functions reducing the capital and operational costs of all IT services, especially cloud computing. The decoupling of network functions from physical devices results in flexible and dynamic resource scheduling, hence, energy efficiency [ 60 ]. Five out of six case studies show that the NFV based networks provide energy savings compared to baseline networks. Similarly, higher performance and energy efficiency were observed as compared to commodity servers while experimenting with a virtualized Deep Packet Inspection (DPI) application [ 59 ]. However, a balance between network function performance and energy efficiency achieved through virtualization has to be resolved.

Both SDN and NFV technologies are in early stages of deployment. Therefore, research on the development of green computing architectures based on SDN and NFV technologies has significant future prospects in terms of integration with other IT technologies. Interested readers can refer to [ 61 ] for a detailed survey on green networks.

7 Practices, research challenges, and issues

In this section, we debate the practices, research issues, and challenges to the green initiatives in emerging IT technologies in particular and computing in general.

Green computing practices emphasize the implementation of green technologies at industrial and organizational level. The cost of per unit energy will rise significantly owing to a considerable decrease in global energy resources. As a result, it has become necessary for both public and government sectors to propose and practice state-of-the-art strategies and plans for green computing [ 62 ]. State-of-the-art green computing practices consider implementation of energy friendly IT equipment, lightweight resource consumption protocols, and disposal of electronic waste [ 63 ]. Green computing practices emphasize turning off IT resources when not utilized for an extended period of time. Green computing practices also schedule IT resources in low system power and idle states. The standby execution mode is applied for saving power if the execution power state is lower than a threshold [ 64 ]. The management of aging IT resources is another important issue in green computing. Older hardware devices have increased power consumption and require resource replacements and disposals. Hence, the practice of recycling needs to be applied to aging IT resources. Similarly, practices limiting the utilization of paper prints should be applied at organizational level [ 4 ]. The research challenges to emerging IT technologies are listed in the paragraphs below.

Green cloud computing: Green cloud computing demands divergence from conventional computing techniques, hence, increased operational and infrastructural costs. For example, renewable energy has a higher cost than conventional grid energy. Similarly, waste heat utilization measures in data centers also demand costly thermal heat exchange materials. Incorporating green measures with cost-efficient business operations is a challenging task in cloud data centers. The efficiency of renewable energy generation and storage mediums needs to be rigorously increased in order to provide comparable business incentives. The cost of VM migrations for resource consolidation over long-haul networks is also a highly debated research issue [ 14 , 65 ]. Moreover, government policies need to be devised that provide incentives to green cloud computing business providers and users.

Green mobile computing: Mobile application energy optimization demands precise estimation accuracy for efficient battery resource usage. Empowering application developers with a fine granular energy estimation tool to estimate the energy behavior of an application at earlier development stages augments device battery lifetime. Existing energy estimation tools such as power tutor, trepn profile, and Nokia energy profiler, run the application on the smartphone to record power states of power models for smartphone components to estimate energy consumption. However, because of low accuracy of fuel gauge sensors within smartphone batteries, the estimate accuracy is limited. Also, the energy estimation time and overhead is high. To challenge the aforementioned issues, there is a need to develop an estimation tool that should offer high estimation accuracy and limited estimation overhead. One possible solution to this problem is to estimate energy consumption based on operational cost (energy and execution time) of different functions within the software. However, due to the non-deterministic nature of smartphone applications, estimation accuracy is significantly affected. Moreover, software operational cost based estimation also requires accurate estimation of code storage location. The weighted probabilistic approach is a possible solution to resolve these issues [ 24 ].

Green big data: Estimation and calculation of energy consumption for big data analytics is challenging. High and rapid analytic demands of big data are only satisfied when an efficient estimation is available. Similarly, for GreenHadoop, it is challenging to estimate the energy and time requirements for a job based upon which scheduling decisions are made. Estimation is also significant in renewable energy technology and thus, requires extensive work from academia and industry. Continuously increasing big data volume requires scalable increment in available analytic resources and cost. However, the concept of green computing suggests sustainability of energy and processing resources. Consequently, big data analytics technology with minimized impact on the environment is highly desirable [ 43 ].

Green IoT: To preserve Green IoT some challenges arise and need to be addressed such as Green IoT Architectures, Green Infrastructure, Green Spectrum Management, Green Communication and Green Security and Quality of Service (QoS) Provisioning [ 37 ]:

Green IoT Architectures: IoT architecture is still under standardization. The committees of standardization are trying to enable communication between heterogeneous networks, containing various types of devices, across various applications. The challenge is that communication protocols and devices should also consider energy-efficiency while performing their duties as anticipated by end users.

Green infrastructure: Providing energy-efficient infrastructure for IoT is considered an important issue towards greening. Green infrastructure can be achieved through a clean-slate redesign approach. Redeploying and adapting existing infrastructure is a complex task.

Green Communication: Communication is one of the influential factors in greening IoTs. Energy efficient communication between IoT nodes faces several challenges, such as supporting energy-efficient communication protocols along with reliable connectivity.

Green Security and QoS Provisioning: Implementation of reliable security and privacy algorithms puts the burden of computation on IoT devices, consequently it increases the energy consumption.

Computing architectures, circuits, protocols, and algorithms are advancing innovations on green challenges faced by IT. Similarly, the efficiencies of the energy systems have also shown reasonable growth over the last decade. The demand and popularity for computing systems, storage devices, and networks has also increased, hence, neutralizing the advances in green computing. While researchers recognize the importance of continued innovations in efficient and sustainable computing and energy systems, industrial practices lag behind in the adoption of green computing. Operational costs of computing systems can significantly decrease on adoption of green computing practices benefiting both service consumers and managers. IT enabled businesses and industries need to comprehend the advantages of green computing in terms of customer value, operational cost sustainability, and environmental sustainability. The future of green computing lies in effective endorsement of green computing practices by IT industries and IT empowered businesses.

8 Conclusion

In this article, a review of the Green Computing paradigm was presented with a focus on emerging IT technologies. Cloud computing, mobile computing, big data analytics, IoT, and software-based networks were identified as the emerging IT technologies driving the current popularity of the IT industry. The demand and social integration of IT technologies is increasing rapidly, hence, increasing the energy consumption. With a renewed focus on the global energy crisis, IT researchers and practitioners have proposed and implemented several algorithms and protocols for the green operation of the IT industry. These algorithms and protocols implement mechanisms such as idle sleep states, energy-aware decision making, and resource scheduling. However, minimizing the energy consumption of a system significantly affects its performance parameters. The energy optimization level for a device highly depends on the use case of the application. Aggressive energy minimization policies effect system durability due to frequent power off and on system routines.

An overall analysis of the state-of-the-art in green computing shows that the green algorithms and protocols are reaching a high level of maturity, and significant efficiencies are possible. In contrast, the study has demonstrated that, in the IT industry, governance is lagging significantly behind, and hence consideration of green practices is a high priority. In particular, green computing practices need to be implemented at the organizational level to complement and enforce the underlying optimization techniques and technologies proposed by researchers. The strength of green computing solutions lies in their diversity, with consideration of low-level processor, memory, and network components for system optimization alongside greedy and evolutionary heuristics. However, again, this must coincide with robust and intelligent strategies that consider the overall performance energy trade-offs in terms of multi-objective optimization. The paper highlights that further research is required to analyze the impact of energy optimization techniques on system performance parameters such as throughput, and response time. This analysis of system performance and energy will lead to more fine-tuned solutions for green computing that will be more acceptable to IT industry governors who prioritize performance parameters rather than energy.

Abbreviations

Autonomic transfer switch

Cloud data center

Complementary metal oxide semiconductor

Cathode ray tube

Dynamic frequency scaling

Green house gases

Internet of things

Information technology

Machine-to-machine

Quality of service

Radio frequency identification

State of charge

Virtual machine

Wireless sensor network

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Acknowledgements

The authors thank the anonymous reviewers for helpful comments that increased the quality of the article.

This work is partially funded by the Malaysian Ministry of Education under the High Impact Research Grant of University of Malaya UM.C/625/1/HIR/MOE/FCSIT/03.

Authors’ contributions

JS contributed towards the sections of Introduction, Green Cloud Computing, and Conclusion. RW wrote the section Green Mobile Computing. AI wrote the section Green IoT. AS contributed towards section Green Big Data Analytics. KN contributed to Green Practices and Green Computing Requirements. AG, AZ, and SK revised the manuscript. In addition, all authors read and approved the work.

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Shuja, J., Ahmad, R.W., Gani, A. et al. Greening emerging IT technologies: techniques and practices. J Internet Serv Appl 8 , 9 (2017). https://doi.org/10.1186/s13174-017-0060-5

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GREEN COMPUTING: TECHNOLOGIES, APPLICATIONS AND CHALLENGES

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This research work focuses on green computing; its technology and basic applications. Green computing is said to have a vital effect on business work and its operations. Businesses seeking a cost-effective way to responsibly recycle large amounts of computer equipment face a more complicated process. They also have the option of contacting the manufacturers and arranging recycling options. However, in cases where the computer equipment comes from a wide variety of manufacturers, it may be more efficient to hire a third-party contractor to handle the recycling arrangements. There exist companies that specialize in corporate computer disposal services both offer disposal and recycling services in compliance with local laws and regulations. Such companies frequently also offer secure data elimination services.

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Stateful Conformer with Cache-based Inference for Streaming Automatic Speech Recognition

In this paper, we propose an efficient and accurate streaming speech recognition model based on the FastConformer architecture. We adapted the FastConformer architecture for streaming applications through: (1) constraining both the look-ahead and past contexts in the encoder, and (2) introducing an activation caching mechanism to enable the non-autoregressive encoder to operate autoregressively during inference. The proposed model is thoughtfully designed in a way to eliminate the accuracy disparity between the train and inference time which is common for many streaming models. Furthermore, our proposed encoder works with various decoder configurations including Connectionist Temporal Classification (CTC) and RNN-Transducer (RNNT) decoders. Additionally, we introduced a hybrid CTC/RNNT architecture which utilizes a shared encoder with both a CTC and RNNT decoder to boost the accuracy and save computation. We evaluate the proposed model on LibriSpeech dataset and a multi-domain large scale dataset and demonstrate that it can achieve better accuracy with lower latency and inference time compared to a conventional buffered streaming model baseline. We also showed that training a model with multiple latencies can achieve better accuracy than single latency models while it enables us to support multiple latencies with a single model. Our experiments also showed the hybrid architecture would not only speedup the convergence of the CTC decoder but also improves the accuracy of streaming models compared to single decoder models.

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