Fig 7 - uploaded by Chukwuka Gideon Monyei
Content may be subject to copyright.
![Global trends in internet traffic, datacenter workloads and datacenter energy use, 2010-2019. Source [62].](profile/Chukwuka-Monyei/publication/355957586/figure/fig5/AS:1087392551317511@1636265995303/Global-trends-in-internet-traffic-datacenter-workloads-and-datacenter-energy-use.png)
Global trends in internet traffic, datacenter workloads and datacenter energy use, 2010-2019. Source [62].
Source publication
Both policymakers and the technology industry need to do more to combat the ever-growing demand for data and its associated energy impacts. In this study, based on novel corporate data, expert interviews, focus groups with members of the public, extensive site visits across Greenland, Iceland and Norway and a literature review, we look at the energ...
Context in source publication
Context 1
... increase in the dependency on digital ICT across all sectors of the modern global economy has led to an increase in the variety, volume, and velocity of data transmitted through digital devices over the internet (Fig. 7). According to the International Energy Agency, "global internet traffic surged by almost 40% between February and mid-April 2020, driven by growth in video streaming, video conferencing, online gaming, and social networking resulting in a 12-fold growth in global internet traffic" [60]. This growth in internet traffic can be ...
Similar publications
Cryptocurrency transaction forensic examinations need to guarantee completeness, confidentiality, and information integrity. Our work presents BlockQuery as a proof of concept blockchain query system for Bitcoin. BlockQuery is capable of detecting transactions generated by Hierarchical Deterministic (HD) wallets that many publicly available tools c...
Citations
... Among the main references that motivate this study is a systematic survey on energy-efficient techniques in sustainable cloud computing [3] abroad review of problems in energy consumption, carbon footprint generated by data centers and the role of the end user in the weight of the information we consume. According to studies cited by Sovacool et al. [6] the data growth rate between the years 2017 to 2022 was 26% being factors such as the automatic playback of videos on social networks such as Facebook®, Instagram® among others, cause an increase in traffic by just touching one of many factors. According to 2020 reports reveal information and communications technologies could account for more than 14% of global greenhouse gas emissions by 2024, which would be equivalent to more than half of the current carbon footprint of the global transport sector [7]. ...
... According to 2020 reports reveal information and communications technologies could account for more than 14% of global greenhouse gas emissions by 2024, which would be equivalent to more than half of the current carbon footprint of the global transport sector [7]. Touching on variables such as cellular communication, the European average of smartphone use is 6 to 12 minutes every hour as pointed out [6]. To this is added the incessant implementation of fifth generation (5G) which offers higher download speed, greater efficiency and more devices connected to the internet, allowing the user to visualize greater interaction and response times in entertainment systems, increase in transmission services, with exponential expectations in data volumes and processing [4]. ...
... The location of data centers is another mitigating factor, which is why many data centers are migrating to Nordic places, in order to take advantage of the cold, in order to keep the system, cool during the day and night [6]. When [3] outdoor air temperature and humidity meet cooling criteria, fans can replace air conditioners, as studies show in data centers based on direct air-cooled containers, a 20.8% decrease from an air-cooled data center. ...
The carbon footprint generated by the information and communications technology (ICT) sector is increasingly significant, emitting greenhouse gases due to high energy consumption, regardless of the way in which energy is generated, the expansion and growth in data centers, as well as the impact generated by the cryptocurrency sector that in the end represents is reflected in greater consumerism, processing, storage, and transport of information that will be somewhere in the world. Current research addresses the problems and the contrast of figures in energy consumption due to the use of a computer, data processing, the role of the user as an internet consumer, the impact of data centers both in carbon footprint, water footprint and soil footprint, the impact of cryptocurrency mining and its contribution to global energy expenditure as well as the ethical debate of new technologies. And finally, the advances in seeking to optimize energy resources, sustainable and conscious for both consumers and service providers, show the trends focused on energy optimization through software and hardware based on a judicious review of research documents.
... As data centers continue to evolve and expand, there are several emerging trends and opportunities for integrating project management with advanced cooling solutions to enhance energy efficiency , Sovacool, Monyei & Upham, 2022. This article explores emerging trends in data center cooling and energy efficiency, potential advancements in project management practices for data center efficiency, and recommendations for further research and adoption of integrated approaches. ...
Data centers play a crucial role in modern digital infrastructure, but their energy consumption and associated carbon footprint are significant concerns. Among the various energy-consuming components of data centers, cooling systems stand out as major contributors. This paper explores the integration of project management principles with advanced cooling solutions to enhance energy efficiency in data centers. By effectively managing projects and implementing innovative cooling technologies, data center operators can achieve significant reductions in energy consumption and operational costs. The integration of project management practices with advanced cooling solutions involves several key steps. First, project management methodologies such as Agile or Waterfall are applied to plan and execute the implementation of advanced cooling technologies. This includes assessing current cooling systems, identifying suitable advanced solutions, and developing a timeline for implementation. Next, advanced cooling solutions such as liquid cooling, containment systems, or free cooling are deployed to reduce the energy required for cooling data center equipment. One of the primary benefits of integrating project management with advanced cooling solutions is enhanced energy efficiency. Advanced cooling technologies can significantly reduce the energy consumed by data center cooling systems, leading to lower operational costs and a smaller carbon footprint. Additionally, the use of project management methodologies ensures that the implementation of these technologies is carried out efficiently and effectively. Furthermore, this integration can lead to improved data center performance and reliability. By optimizing cooling systems, data center operators can ensure that IT equipment operates within optimal temperature ranges, reducing the risk of overheating and downtime. Additionally, the implementation of advanced cooling solutions can future-proof data centers against increasing heat loads from high-density computing equipment. In conclusion, integrating project management with advanced cooling solutions offers a promising path to enhance energy efficiency in data centers. By adopting this approach, data center operators can achieve significant energy savings, reduce their environmental impact, and improve overall operational efficiency.
... The history of project management strategies for implementing energy-efficient cooling solutions in emerging data center markets reflects a gradual evolution driven by technological advancements, economic factors, and environmental concerns (Sovacool, Monyei & Upham, 2022, Zhu, et. al., 2023. ...
Project management strategies play a pivotal role in implementing energy-efficient cooling solutions in emerging data center markets. This review explores key strategies for successful implementation, focusing on the unique challenges and opportunities in these markets. Emerging data center markets are characterized by rapid growth and evolving infrastructure needs. As such, implementing energy-efficient cooling solutions requires a strategic approach that addresses local regulatory requirements, environmental conditions, and technological capabilities. Effective project management in these contexts involves several key strategies. Firstly, thorough planning is essential. This includes conducting feasibility studies, assessing site-specific requirements, and developing detailed project plans. Understanding the local context is crucial, as it can impact the selection and implementation of cooling solutions. Secondly, stakeholder engagement is critical. Engaging with local authorities, communities, and industry stakeholders can help ensure regulatory compliance and garner support for the project. Additionally, involving local experts and partners can provide valuable insights and enhance the project's success. Thirdly, implementing robust monitoring and evaluation mechanisms is essential. Monitoring energy consumption, cooling system performance, and environmental impacts allows for timely adjustments and optimizations, ensuring the long-term sustainability of the cooling solutions. Lastly, knowledge sharing and capacity building are key components of successful project management in emerging data center markets. Providing training and education on energy-efficient practices and technologies can help build local capacity and ensure the continued success of the cooling solutions. In conclusion, project management strategies for implementing energy-efficient cooling solutions in emerging data center markets require a holistic approach that considers local context, engages stakeholders, implements robust monitoring mechanisms, and promotes knowledge sharing. By adopting these strategies, data center operators can effectively address the challenges and opportunities in these markets, contributing to a more sustainable and resilient data center infrastructure.
... They have theoretically analysed the impact of community use of the Internet on energy consumption. The establishment of the energy Internet helps to achieve energy conservation, emission reduction and energy efficiency improvement in communities Sovacool et al., 2022). The use of the Internet can promote the transformation of household energy consumption by increasing the proportion of non-agricultural employment (Zhang et al., 2023a(Zhang et al., , 2023b(Zhang et al., , 2023c. ...
As an important source of global climate change, household energy consumption can successfully climb the energy ladder, which is the key to promoting sustainable development. In the digital age, the use of the Internet has provided new avenues for the energy transformation of households in developing countries. This study aims to use micro household data in China to explore the impact and underlying mechanisms of Internet use on household energy ladder, and provide experience for other similar countries. The results indicate that, first, Internet use can promote Chinese households to climb the energy ladder. The use of the Internet can reduce the probability of using initial energy and traditional energy, and improve the probability of using high-quality energy. Second, Internet use can weaken the phenomenon of energy stacking, reduce the consumption of straw, firewood, coal, and diesel, and increase the consumption of natural gas in pipelines. Third, the role of the Internet in households climbing the energy ladder exhibits regional heterogeneity. Compared to households in the eastern, urban, and central heating systems, the western, rural, and separate-household heating households have better results. Fourth, Internet use can achieve household energy ladder climbing by reducing social travel, increasing environmental awareness, and increasing happiness. Fifth, China’s peak valley electricity prices and tiered electricity pricing policies are not conducive to unleashing the energy transformation dividends of the Internet. In response to these research findings, this article proposes five suggestions to contribute more Chinese experience to promoting sustainable development of household energy use.
... These owners are looking for possibilities to improve energy efficiency to reduce the datacenter operating cost [8][9][10][11][12]. Moreover, they make a power purchase agreement with the electricity providers [13]. On the other hand, the commercial sector is the most electricity consumer globally, with Alphabet (Google), Microsoft and Facebook at the top list [14]. ...
Datacenters are the primary driving force behind the energy shift to renewable sources. This shift limits the environmental impact of non-renewable sources and decarbonizes datacenters. However, renewable energy generation fluctuates with time and location, which introduces uncertainty in fulfilling user requests (URs). Thus, non-renewable energy generation continues to power the datacenters to make stability. Recent works direct to the use of renewable energy generation followed by non-renewable generation while assigning the URs to the resources of the datacenters. These works do not model the uncertainty of renewable and non-renewable energy resources and the level of uncertainty. This paper extends the three benchmark algorithms, namely future-aware best-fit (FABEF), highest available renewable energy first (HAREF) and round-robin (RR), by incorporating uncertainty and its level (UNL), and we call them UNL-FABEF, UNL-HAREF and UNL-RR, respectively. The goal of UNL-FABEF is to minimize the overall cost, whereas UNL-HAREF is to maximize the available renewable energy usage. On the contrary, UNL-RR assigns the datacenter to the URs in a roundabout fashion. This paper also introduces the UNL multi-objective scheduling algorithm (UNL-MOSA) to make a trade-off between UNL-FABEF and UNL-HAREF. UNL-MOSA creates a balance between the overall cost and the available renewable energy usage. All four algorithms consider three UNLs of URs, namely low, medium and high, for renewable energy resources. These algorithms are tested using fifty instances of ten datasets with 200 to 2000 URs and 20 to 200 datacenters and compared using five performance metrics: the overall cost, number of used renewable and non-renewable energy resource slots, and uncertainty cost and time. The performance of four algorithms in these performance metrics is extensively examined to know their applicability.
... Data centers exemplify infrastructure's generative contradictions and ambiguities -being at once both local and material, global and intangible -which complicate the analyst's task of interpreting the conflicting visions and interests expressed by its societal stakeholders (cf. Sovacool et al., 2022a;Williams et al., 2022). ...
... In a proposal for "relational" footprinting, Pasek et al. (2023) therefore call to increased attention to how data centers' socio-ecological impacts are relative across geographical contexts, such as when the needs for water or electricity for cooling differ between data centers located in arctic and warmer climates. Such climatic prerequisites have been a central argument in Norway's recent positioning to become a global data center hub (Pickren, 2018;Sovacool et al., 2022a;Upham et al., 2022), where a cool climate and access to renewable energy are used as arguments for a sustainable data-driven economy (KMD, 2021). Norway became the first country to launch its own data center strategy in 2018 and has taken aggressive steps to attract industry investors, such as by reducing electricity fees for the industry. ...
... Furthermore, there is already a vast amount of surplus heat available from existing industries in Norway (≈20 TWh according to estimates (Kauko et al. 2022)). Despite possible strategies for heat reuse (Sovacool et al., 2022a;, the difficulties of finding or establishing enough heat-demanding industries and connecting these to heat producers are imminent even with the existing industry in Norway (cf. Enova, 2009;Johansen and Røyrvik, 2021), and adding data centers producing additional surplus heat would essentially further increase that gap. ...
Physical infrastructures such as data centers constitute the material underpinnings of digitalization and are, like emerging digital tools and products, part of the mechanics of digital transformation. Research on data centers could yield insights into digital transformation dynamics and inform emerging normative frameworks for digital sufficiency in support of sustainability. Norway offers a rich case for study, given its ongoing efforts to become a global network hub in step with its digital transformation. We ask: How might a paradox approach a) explain current controversies around Norwegian data centers and b) help situate sociotechnical understandings of infrastructure within emerging ethical frameworks for societal digital transformation? Based on an exploratory study of local, national, and professional media narratives during Europe's energy crisis in 2022, we analyze digital infrastructure's attendant contradictions and new uncertainties using a paradox approach. We highlight gaps in, and suggest future research priorities for, normative ethical frameworks for digital sufficiency.
... A global digital transformation would need an unprecedented level of machine intelligence. Making this machine intelligence sustainable and aligning it with planetary health challenges is a grand challenge on its own, starting with the rapid reduction of GHG emissions associated with the internet and currently carbonintensive data centers 1,2 . The literature emphasizes several ways in which AI can play a crucial role in addressing climate change. ...
The ongoing global race for bigger and better artificial intelligence (AI) systems is expected to have a profound societal and environmental impact by altering job markets, disrupting business models, and enabling new governance and societal welfare structures that can affect global consensus for climate action pathways. However, the current AI systems are trained on biased datasets that could destabilize political agencies impacting climate change mitigation and adaptation decisions and compromise social stability, potentially leading to societal tipping events. Thus, the appropriate design of a less biased AI system that reflects both direct and indirect effects on societies and planetary challenges is a question of paramount importance. In this paper, we tackle the question of data-centric knowledge generation for climate action in ways that minimize biased AI. We argue for the need to co-align a less biased AI with an epistemic web on planetary health challenges for more trustworthy decision-making. A human-in-the-loop AI can be designed to align with three goals. First, it can contribute to a planetary epistemic web that supports climate action. Second, it can directly enable mitigation and adaptation interventions through knowledge of social tipping elements. Finally, it can reduce the data injustices associated with AI pretraining datasets.
... The pulp and paper industry (PPI) is among the top five energy-consuming industries, and its energy use has been increasing at an average annual rate of 0.3% between 2018 and 2000. The PPI accounts for 6% of global industrial energy consumption and 2% of direct industrial CO2 emissions [2,3]. However, despite these significant contributions to carbon emissions, production in the PPI is expected to continue to increase. ...
This paper presents a steam energy modeling for paper-making, focusing on factory energy management in the paper drying process and analysis of the response characteristics of the process. A heuristic methodology is developed to model steam energy consumption by efficiently and robustly solving correlations among significant factors. Using paper production and operation data, independent variables were selected to identify the relationships among influencing factors. Data on paper breakage and branching were extracted from facility operation data and analyzed using Spearman correlation. Results of this study showed that the type of paper-making, the cylinder of the drying process, and the pressure and temperature of the steam energy supply were identified as the main influencing factors. Feature factors were extracted, classified into support vector machine (SVM) models, and analyzed using t-distributed stochastic neighbor embedding (T-SNE) dimensionality reduction. The proposed clustering modeling methodology can solve specific problems by extracting additional generalized factors and improving energy efficiency. Based on the analysis of actual process operation data, it is found that controlling steam pressure applied to the cylinder is a significant variable that can reduce steam energy consumption while maintaining appropriate quality. Overall, this study provides valuable insights into the energy management of the paper drying process and offers a methodology for modeling steam energy consumption in paper-making that can be applied to other industrial processes.
... 2 inclusive innovation across societies as well as within them (McCauley and Heffron, 2018;Sovacool et al., 2022b;Wang and Lo, 2021). ...
... This situation is bringing more attention to data center organization and optimization [2] in order to improve their performance in IoT environments [3]. In this context, machine learning techniques may help optimize performance by achieving increased efficiency in resource usage [4], leading to a decrease in energy consumption [5], which may lower the carbon footprint [6] in order to make the IoT environments more sustainable [7] and resilient [8]. ...
Data center organization and optimization are increasingly receiving attention due to the ever-growing deployments of edge and fog computing facilities. The main aim is to achieve a topology that processes the traffic flows as fast as possible and that does not only depend on AI-based computing resources, but also on the network interconnection among physical hosts. In this paper, graph theory is introduced, due to its features related to network connectivity and stability, which leads to more resilient and sustainable deployments, where cage graphs may have an advantage over the rest. In this context, the Petersen graph cage is studied as a convenient candidate for small data centers due to its small number of nodes and small network diameter, thus providing an interesting solution for edge and fog data centers.
