Relevant bibliographies by topics / Green Computing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Green Computing'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 June 2024

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Journal articles on the topic "Green Computing"

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Tailor, Jinal H. "Green it: Sustainability Plan of Green Computing." Global Journal For Research Analysis 3, no. 4 (June 15, 2012): 38–39. http://dx.doi.org/10.15373/22778160/apr2014/13.

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Sharma, Er Kusum, and ErRajeev Sharma. "GREEN COMPUTING." International Journal of Advanced Research 4, no. 9 (September 30, 2016): 481–83. http://dx.doi.org/10.21474/ijar01/1505.

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Noordin, Mohamad Fauzan. "Green Computing." International Journal of Green Computing 6, no. 1 (January 2015): 33–39. http://dx.doi.org/10.4018/ijgc.2015010103.

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Green Computing has attracted concerns in the past few decades, with the sole concern for the procedures involve in the designing, manufacturing, usage and disposal of information and communication technology (ICT) basically for environmental sustainability. The importance of human factor in green computing has not received the deserved attention. The social, moral and ethical decadence resulting from the use of ICT has reduced the quality of human in modern time. This study advocates the importance of peopleware as essential aspect of green computing which equalling demands concern. Thus the adoption of peopleware in green computing practices will invariably improve sustainability of the physical and spiritual environment.
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A M, Bagul, and Saylee Shitole. "Green Computing." IJARCCE 6, no. 3 (March 30, 2017): 341–42. http://dx.doi.org/10.17148/ijarcce.2017.6377.

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Jagwinder Singh. "Green Computing." Journal of Technology Management for Growing Economies 7, no. 2 (October 24, 2016): 7–26. http://dx.doi.org/10.15415/jtmge.2016.72003.

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Green Computing is the concept to improve environmental conditions. Green Computing has begun to spread in the past few years, gaining increasingly popularity. It includes manufacturing, designing and efficiently using computers and associated subsystems- such as LED,RAM, Processor, Mouse and Keyboard with minimum impact on the environment. We are doing the study and practice of developing Information Technology resources in an environment friendly manner such that our environment get protected. As we all know usage of electronic devices is very popular these days, of which Laptops and Tabs are the most common. Surveys were conducted to analyze power consumption, pattern of devices, components like RAM, Rom processors and LED displays. Furthermore recommendations were made to make them more energy efficient. To promote the idea of green computing, energy star rating were introduced for electronic devices. The more is the star rating, the less is the power consumption. This type of computing can facilitate us to safe and make healthy environment all over the world. Present study will help us to take some initiatives to save vast amount of power which is wasted on very large scale currently under the field of computers/ electronics industry.
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Jena, Rabindra Ku, and D. G. Dey. "Green Computing." International Journal of Knowledge Society Research 2, no. 1 (January 2011): 61–70. http://dx.doi.org/10.4018/jksr.2011010105.

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Information technology (IT) is playing an increasingly important role in both business and individuals’ private lives. It is also consuming ever greater amounts of energy; therefore, it is a significant source of CO² emissions. Thus, environmental and energy conservation issues have gained attention in recent years. The reality of rising energy costs and their impact on international affairs coupled with the increased concern over the global warming climate crisis and other environmental issues have shifted the social and economic consciousness of modern society. This paper discusses different aspects of green computing and its impact in India.
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Kurp, Patrick. "Green computing." Communications of the ACM 51, no. 10 (October 2008): 11–13. http://dx.doi.org/10.1145/1400181.1400186.

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Saha, Biswajit. "Green Computing." International Journal of Computer Trends and Technology 14, no. 2 (August 25, 2014): 46–50. http://dx.doi.org/10.14445/22312803/ijctt-v14p112.

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Landwehr, C. E. "Green Computing." IEEE Security and Privacy Magazine 3, no. 6 (November 2005): 3. http://dx.doi.org/10.1109/msp.2005.148.

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Atrey, Ankita, Nikita Jain, and N. Ch S. N. Iyengar. "A Study on Green Cloud Computing." International Journal of Grid and Distributed Computing 6, no. 6 (December 31, 2013): 93–102. http://dx.doi.org/10.14257/ijgdc.2013.6.6.08.

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Dissertations / Theses on the topic "Green Computing"

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Franci, Alessandro. "Green Cloud Computing: una rassegna comparativa." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2010. http://amslaurea.unibo.it/1181/.

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La rapida crescita di Internet e del numero di host connessi sta portando sempre di più alla nascita di nuove forme di tecnlogie ed applicazioni serverside, facendo del client un thin-client. Il Cloud Computing offre una valida piattaforma a queste nuove tecnologie, ma esso si deve confrontare con diverse problematiche, fra cui la richiesta energetica sempre più crescente, che si ripercuote su un'inevitabile aumento dei gas serra prodotti indirettamente. In questa tesi analizzeremo i problemi energetici legati al Cloud Computing e le possibili soluzioni, andando infine a creare una tassonomia fra i diversi Cloud Computing più importanti sul mercato attuale.
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Talebi, Mujtaba. "Computer power consumption benchmarking for green computing." Click here for download, 2008. http://proquest.umi.com/pqdweb?did=1490085671&sid=1&Fmt=2&clientId=3260&RQT=309&VName=PQD.

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Bi, Mingsong. "OPTIMIZING PROCESSOR AND MEMORY FOR GREEN COMPUTING." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/205424.

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Energy efficiency has become one of the most important factors in the development of computer systems. Increasingly power-hungry processors and memory subsystem have reinforced the need for aggressive power management. Dynamic voltage scaling has become a common consideration for designing energy efficient CPUs in systems ranging from portable devices to large-scale systems. As applications become more data centric and put more pressure on memory subsystem, managing energy consumption of main memory is also becoming critical. Subsequently in this dissertation, we address the issues in designing energy efficient CPU and memory for personal computing devices as well as large-scale systems.For large-scale systems, we address memory subsystem dedicated to buffer cache which accounts for the majority of memory usage in server environment. We take advantage of the I/O handling routines in the OS kernel to hide the delay incurred by the memory state transition so that performance degradation is minimized while high energy savings is achieved. We also address interactive workloads, which account for the bulk of the processing demand on modern mobile or desktop systems. We propose Interaction-Aware Dynamic Voltage Scaling (IADVS) for CPU and Interaction-Aware Memory Energy Management (IAMEM) for memory. The IA framework relies on automatic correlation of user-initiated tasks with the demand placed on CPU and memory to accurately predict power states for CPU and memory. Both mechanisms achieve maximal energy savings while minimizing the impact on the application's performance.
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Adhinarayanan, Vignesh. "Models and Techniques for Green High-Performance Computing." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/98660.

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High-performance computing (HPC) systems have become power limited. For instance, the U.S. Department of Energy set a power envelope of 20MW in 2008 for the first exascale supercomputer now expected to arrive in 2021--22. Toward this end, we seek to improve the greenness of HPC systems by improving their performance per watt at the allocated power budget. In this dissertation, we develop a series of models and techniques to manage power at micro-, meso-, and macro-levels of the system hierarchy, specifically addressing data movement and heterogeneity. We target the chip interconnect at the micro-level, heterogeneous nodes at the meso-level, and a supercomputing cluster at the macro-level. Overall, our goal is to improve the greenness of HPC systems by intelligently managing power. The first part of this dissertation focuses on measurement and modeling problems for power. First, we study how to infer chip-interconnect power by observing the system-wide power consumption. Our proposal is to design a novel micro-benchmarking methodology based on data-movement distance by which we can properly isolate the chip interconnect and measure its power. Next, we study how to develop software power meters to monitor a GPU's power consumption at runtime. Our proposal is to adapt performance counter-based models for their use at runtime via a combination of heuristics, statistical techniques, and application-specific knowledge. In the second part of this dissertation, we focus on managing power. First, we propose to reduce the chip-interconnect power by proactively managing its dynamic voltage and frequency (DVFS) state. Toward this end, we develop a novel phase predictor that uses approximate pattern matching to forecast future requirements and in turn, proactively manage power. Second, we study the problem of applying a power cap to a heterogeneous node. Our proposal proactively manages the GPU power using phase prediction and a DVFS power model but reactively manages the CPU. The resulting hybrid approach can take advantage of the differences in the capabilities of the two devices. Third, we study how in-situ techniques can be applied to improve the greenness of HPC clusters. Overall, in our dissertation, we demonstrate that it is possible to infer power consumption of real hardware components without directly measuring them, using the chip interconnect and GPU as examples. We also demonstrate that it is possible to build models of sufficient accuracy and apply them for intelligently managing power at many levels of the system hierarchy.
Doctor of Philosophy
Past research in green high-performance computing (HPC) mostly focused on managing the power consumed by general-purpose processors, known as central processing units (CPUs) and to a lesser extent, memory. In this dissertation, we study two increasingly important components: interconnects (predominantly focused on those inside a chip, but not limited to them) and graphics processing units (GPUs). Our contributions in this dissertation include a set of innovative measurement techniques to estimate the power consumed by the target components, statistical and analytical approaches to develop power models and their optimizations, and algorithms to manage power statically and at runtime. Experimental results show that it is possible to build models of sufficient accuracy and apply them for intelligently managing power on multiple levels of the system hierarchy: chip interconnect at the micro-level, heterogeneous nodes at the meso-level, and a supercomputing cluster at the macro-level.
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Safieddine, Ibrahim. "Optimisation d'infrastructures de cloud computing sur des green datacenters." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAM083/document.

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Les centres de données verts de dernière génération ont été conçus pour une consommation optimisée et une meilleure qualité du niveau de service SLA. Cependant,ces dernières années, le marché des centres de données augmente rapidement,et la concentration de la puissance de calcul est de plus en plus importante, ce qui fait augmenter les besoins en puissance électrique et refroidissement. Un centre de données est constitué de ressources informatiques, de systèmes de refroidissement et de distribution électrique. De nombreux travaux de recherche se sont intéressés à la réduction de la consommation des centres de données afin d'améliorer le PUE, tout en garantissant le même niveau de service. Certains travaux visent le dimensionnement dynamique des ressources en fonction de la charge afin de réduire le nombre de serveurs démarrés, d'autres cherchent à optimiser le système de refroidissement qui représente un part important de la consommation globale.Dans cette thèse, afin de réduire le PUE, nous étudions la mise en place d'un système autonome d'optimisation globale du refroidissement, qui se base sur des sources de données externes tel que la température extérieure et les prévisions météorologiques, couplé à un module de prédiction de charge informatique globale pour absorber les pics d'activité, pour optimiser les ressources utilisés à un moindre coût, tout en préservant la qualité de service. Afin de garantir un meilleur SLA, nous proposons une architecture distribuée pour déceler les anomalies de fonctionnements complexes en temps réel, en analysant de gros volumes de données provenant des milliers de capteurs du centre de données. Détecter les comportements anormaux au plus tôt, permet de réagir plus vite face aux menaces qui peuvent impacter la qualité de service, avec des boucles de contrôle autonomes qui automatisent l'administration. Nous évaluons les performances de nos contributions sur des données provenant d'un centre de donnée en exploitation hébergeant des applications réelles
Next-generation green datacenters were designed for optimized consumption and improved quality of service level Service Level Agreement (SLA). However, in recent years, the datacenter market is growing rapidly, and the concentration of the computing power is increasingly important, thereby increasing the electrical power and cooling consumptions. A datacenter consists of computing resources, cooling systems, and power distribution. Many research studies have focused on reducing the consumption of datacenters to improve the PUE, while guaranteeing the same level of service. Some works aims the dynamic sizing of resources according to the load, to reduce the number of started servers, others seek to optimize the cooling system which represents an important part of total consumption. In this thesis, in order to reduce the PUE, we study the design of an autonomous system for global cooling optimization, which is based on external data sources such as the outside temperature and weather forecasting, coupled with an overall IT load prediction module to absorb the peaks of activity, to optimize activere sources at a lower cost while preserving service level quality. To ensure a better SLA, we propose a distributed architecture to detect the complex operation anomalies in real time, by analyzing large data volumes from thousands of sensors deployed in the datacenter. Early identification of abnormal behaviors, allows a better reactivity to deal with threats that may impact the quality of service, with autonomous control loops that automate the administration. We evaluate the performance of our contributions on data collected from an operating datacenter hosting real applications
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Tapou, Monaf Sabri. "High efficiency smart voltage regulating module for green mobile computing." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/8285.

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In this thesis a design for a smart high efficiency voltage regulating module capable of supplying the core of modern microprocessors incorporating dynamic voltage and frequency scaling (DVS) capability is accomplished using a RISC based microcontroller to facilitate all the functions required to control, protect, and supply the core with the required variable operating voltage as set by the DVS management system. Normally voltage regulating modules provide maximum power efficiency at designed peak load, and the efficiency falls off as the load moves towards lesser values. A mathematical model has been derived for the main converter and small signal analysis has been performed in order to determine system operation stability and select a control scheme that would improve converter operation response to transients and not requiring intense computational power to realize. A Simulation model was built using Matlab/Simulink and after experimenting with tuned PID controller and fuzzy logic controllers, a simple fuzzy logic control scheme was selected to control the pulse width modulated converter and several methods were devised to reduce the requirements for computational power making the whole system operation realizable using a low power RISC based microcontroller. The same microcontroller provides circuit adaptations operation in addition to providing protection to load in terms of over voltage and over current protection. A novel circuit technique and operation control scheme enables the designed module to selectively change some of the circuit elements in the main pulse width modulated buck converter so as to improve efficiency over a wider range of loads. In case of very light loads as the case when the device goes into standby, sleep or hibernation mode, a secondary converter starts operating and the main converter stops. The secondary converter adapts a different operation scheme using switched capacitor technique which provides high efficiency at low load currents. A fuzzy logic control scheme was chosen for the main converter for its lighter computational power requirement promoting implementation using ultra low power embedded controllers. Passive and active components were carefully selected to augment operational efficiency. These aspects enabled the designed voltage regulating module to operate with efficiency improvement in off peak load region in the range of 3% to 5%. At low loads as the case when the computer system goes to standby or sleep mode, the efficiency improvent is better than 13% which will have noticeable contribution in extending battery run time thus contributing to lowering the carbon footprint of human consumption.
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Sacchetti, Nicola. "Green information technology." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2011. http://amslaurea.unibo.it/1861/.

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Quando si parla di green information technology si fa riferimento a un nuovo filone di ricerche focalizzate sulle tecnologie ecologiche o verdi rivolte al rispetto ambientale. In prima battuta ci si potrebbe chiedere quali siano le reali motivazioni che possono portare allo studio di tecnologie green nel settore dell’information technology: sono così inquinanti i computer? Non sono le automobili, le industrie, gli aerei, le discariche ad avere un impatto inquinante maggiore sull’ambiente? Certamente sì, ma non bisogna sottovalutare l’impronta inquinante settore IT; secondo una recente indagine condotta dal centro di ricerche statunitense Gartner nel 2007, i sistemi IT sono tra le maggiori fonti di emissione di CO2 e di altri gas a effetto serra , con una percentuale del 2% sulle emissioni totali del pianeta, eguagliando il tasso di inquinamento del settore aeromobile. Il numero enorme di computer disseminato in tutto il mondo assorbe ingenti quantità di energia elettrica e le centrali che li alimentano emettono tonnellate di anidride carbonica inquinando l’atmosfera. Con questa tesi si vuole sottolineare l’impatto ambientale del settore verificando, attraverso l’analisi del bilancio sociale ed ambientale, quali misure siano state adottate dai leader del settore informatico. La ricerca è volta a dimostrare che le più grandi multinazionali informatiche siano consapevoli dell’inquinamento prodotto, tuttavia non adottano abbastanza soluzioni per limitare le emissioni, fissando futili obiettivi futuri.
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Letcher, Charles. "Green computing - Desktop computer power management at the city of Tulsa." Thesis, Oklahoma State University, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=1542205.

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One type of Green Computing focuses on reducing power consumption of computers. Specialized software like 1E/Nightwatchman aids in reducing the power consumption of desktop computers by placing them in a low power state when not in use. This thesis describes the implementation of 1E/Nightwatchman power management software on two thousand desktop computers at the City of Tulsa. It shows the method used to predict power savings of $100,000.00 per year and compares the prediction to the actual savings after one year of operation.

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Aldawsari, B. M. A. "An energy-efficient multi-cloud service broker for green cloud computing environment." Thesis, Liverpool John Moores University, 2018. http://researchonline.ljmu.ac.uk/7954/.

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The heavy demands on cloud computing resources have led to a substantial growth in energy consumption of the data transferred between cloud computing parties (i.e., providers, datacentres, users, and services) and in datacentre’s services due to the increasing loads on these services. From one hand, routing and transferring large amounts of data into a datacentre located far from the user’s geographical location consume more energy than just processing and storing the same data on the cloud datacentre. On the other hand, when a cloud user submits a job (in the form of a set of functional and non-functional requirements) to a cloud service provider (aka, datacentre) via a cloud services broker; the broker becomes responsible to find the best-fit service to the user request based mainly on the user’s requirements and Quality of Service (QoS) (i.e., response time, latency). Hence, it becomes a high necessity to locate the lowest energy consumption route between the user and the designated datacentre; and the minimum possible number of most energy efficient services that satisfy the user request. In fact, finding the most energy-efficient route to the datacentre, and most energy efficient service(s) to the user are the biggest challenges of multi-cloud broker’s environment. This thesis presents and evaluates a novel multi-cloud broker solution that contains three innovative models and their associated algorithms. The first one is aimed at finding the most energy efficient route, among multiple possible routes, between the user and cloud datacentre. The second model is to find and provide the lowest possible number of most energy efficient services in order to minimise data exchange based on a bin-packing approach. The third model creates an energy-aware composition plan by integrating the most energy efficient services, in order to fulfil user requirements. The results demonstrated a favourable performance of these models in terms of selecting the most energy efficient route and reaching the least possible number of services for an optimum and energy efficient composition.
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McClurg, Josiah. "Fast demand response with datacenter loads: a green dimension of big data." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5811.

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Demand response is one of the critical technologies necessary for allowing large-scale penetration of intermittent renewable energy sources in the electric grid. Data centers are especially attractive candidates for providing flexible, real-time demand response services to the grid because they are capable of fast power ramp-rates, large dynamic range, and finely-controllable power consumption. This thesis makes a contribution toward implementing load shaping with server clusters through a detailed experimental investigation of three broadly-applicable datacenter workload scenarios. We experimentally demonstrate the eminent feasibility of datacenter demand response with a distributed video transcoding application and a simple distributed power controller. We also show that while some software power capping interfaces performed better than others, all the interfaces we investigated had the high dynamic range and low power variance required to achieve high quality power tracking. Our next investigation presents an empirical performance evaluation of algorithms that replace arithmetic operations with low-level bit operations for power-aware Big Data processing. Specifically, we compare two different data structures in terms of execution time and power efficiency: (a) a baseline design using arrays, and (b) a design using bit-slice indexing (BSI) and distributed BSI arithmetic. Across three different datasets and three popular queries, we show that the bit-slicing queries consistently outperform the array algorithm in both power efficiency and execution time. In the context of datacenter power shaping, this performance optimization enables additional power flexibility -- achieving the same or greater performance than the baseline approach, even under power constraints. The investigation of read-optimized index queries leads up to an experimental investigation of the tradeoffs among power constraint, query freshness, and update aggregation size in a dynamic big data environment. We compare several update strategies, presenting a bitmap update optimization that allows improved performance over both a baseline approach and an existing state-of-the-art update strategy. Performing this investigation in the context of load shaping, we show that read-only range queries can be served without performance impact under power cap, and index updates can be tuned to provide a flexible base load. This thesis concludes with a brief discussion of control implementation and summary of our findings.
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Books on the topic "Green Computing"

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De, Debashis, Anwesha Mukherjee, and Rajkumar Buyya, eds. Green Mobile Cloud Computing. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08038-8.

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Gruber, Ralf. HPC@green IT: Green high performance computing methods. Heidelberg: Springer, 2010.

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Chowdhary, Chiranji Lal, Mahasweta Sarkar, and Sanjaya Kumar Panda. Cognitive Computing Using Green Technologies. Edited by Asis Kumar Tripathy. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis Group, LLC, 2021. | Series: Green energy and technology : Concepts and applications: CRC Press, 2021. http://dx.doi.org/10.1201/9781003121619.

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Sharma, Deepak Kumar, Koyel Datta Gupta, and Rinky Dwivedi. Green Computing in Network Security. New York: CRC Press, 2021. http://dx.doi.org/10.1201/9781003097198.

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Miani, Rodrigo, Lasaro Camargos, Bruno Zarpelão, Erika Rosas, and Rafael Pasquini, eds. Green, Pervasive, and Cloud Computing. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19223-5.

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Yu, Zhiwen, Christian Becker, and Guoliang Xing, eds. Green, Pervasive, and Cloud Computing. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64243-3.

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Huang, Xinyi, Yang Xiang, and Kuan-Ching Li, eds. Green, Pervasive, and Cloud Computing. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39077-2.

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Kawahara, Takayuki, and Hiroyuki Mizuno, eds. Green Computing with Emerging Memory. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-0812-3.

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Au, Man Ho Allen, Arcangelo Castiglione, Kim-Kwang Raymond Choo, Francesco Palmieri, and Kuan-Ching Li, eds. Green, Pervasive, and Cloud Computing. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57186-7.

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Li, Shijian, ed. Green, Pervasive, and Cloud Computing. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15093-8.

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Book chapters on the topic "Green Computing"

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Roy, Siddhartha. "Green Computing." In Go Green for Environmental Sustainability, 87–100. First edition. | Boca Raton : CRC Press, 2021.: CRC Press, 2021. http://dx.doi.org/10.1201/9781003055020-07.

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Pruhs, Kirk. "Green Computing Algorithmics." In Lecture Notes in Computer Science, 161–83. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91908-9_10.

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Mehra, N., D. Kapil, A. Bansal, and N. Punera. "Green Cloud Computing." In Green Internet of Things for Smart Cities, 263–77. New York: CRC Press, 2021. http://dx.doi.org/10.1201/9781003032397-17.

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Kumar, Amit. "Green Cloud Computing." In Cloud-based Intelligent Informative Engineering for Society 5.0, 193–206. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003213895-11.

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Aman, Bogdan, Erzsébet Csuhaj-Varjú, and Rudolf Freund. "Red–Green P Automata." In Membrane Computing, 139–57. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14370-5_9.

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Priyanka, Mysha Maliha, K. M. Safin Kamal, Md Hasibur Rahman, Ayesha Siddiqa Yasmin, Ahmed Wasif Reza, and Mohammad Shamsul Arefin. "Green Task Scheduling Algorithm in Green-Cloud." In Intelligent Computing and Optimization, 75–87. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-50151-7_8.

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Cato, Andrea, and Herbert Schreiber. "Climate Save Computing – Energieund Ressourceneffizienz in der IT." In Green Office, 213–26. Wiesbaden: Gabler, 2010. http://dx.doi.org/10.1007/978-3-8349-8952-9_15.

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Kumar, Saurabh, and Rajkumar Buyya. "Green Cloud Computing and Environmental Sustainability." In Harnessing Green It, 315–39. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118305393.ch16.

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Panigrahi, Satya Sobhan, Bibhuprasad Sahu, Amrutanshu Panigrahi, and Sachi Nandan Mohanty. "Green Cloud Computing: An Emerging Trend of GIT in Cloud Computing." In Green Engineering and Technology, 225–42. First edition. | Boca Raton : CRC Press, 2021. |: CRC Press, 2021. http://dx.doi.org/10.1201/9781003176275-13.

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Ullah, Saif, K. P. Arjun, and Anju P. Sureshbabu. "Green Computing and Blockchain Fundamentals." In Handbook of Green Computing and Blockchain Technologies, 1–16. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003107507-1.

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Conference papers on the topic "Green Computing"

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Pedram, Massoud. "Green computing." In the 19th ACM Great Lakes symposium. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1531542.1531543.

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Vikram, Shweta. "Green computing." In 2015 International Conference on Green Computing and Internet of Things (ICGCIoT). IEEE, 2015. http://dx.doi.org/10.1109/icgciot.2015.7380566.

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Jones, Alex K. "Green Computing." In GLSVLSI '17: Great Lakes Symposium on VLSI 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3060403.3066859.

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Gayathri, B. "Green Cloud Computing." In IET Chennai 3rd International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/cp.2012.2201.

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Heinisch, Philip, Katharina Ostaszewski, and Hendrik Ranocha. "Towards Green Computing." In IWOCL '20: International Workshop on OpenCL. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3388333.3403035.

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Pruhs, Kirk. "Green Computing Algorithmics." In 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 2011. http://dx.doi.org/10.1109/focs.2011.44.

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Shree, Tanu, Rajiv Kumar, and Nikhil Kumar. "Green Computing in Cloud Computing." In 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). IEEE, 2020. http://dx.doi.org/10.1109/icacccn51052.2020.9362822.

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Jain, A., M. Mishra, S. K. Peddoju, and N. Jain. "Energy efficient computing- Green cloud computing." In 2013 International Conference on Energy Efficient Technologies for Sustainability (ICEETS). IEEE, 2013. http://dx.doi.org/10.1109/iceets.2013.6533519.

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Ba, He, Wendi Heinzelman, Charles-Antoine Janssen, and Jiye Shi. "Mobile computing - A green computing resource." In 2013 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2013. http://dx.doi.org/10.1109/wcnc.2013.6555295.

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Chu, Feng-Seng, Kwang-Cheng Chen, and Chen-Mou Cheng. "Toward green cloud computing." In the 5th International Confernece. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1968613.1968651.

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Reports on the topic "Green Computing"

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Pogozelski, Wendy, Salvatore Priore, Matthew Bernard, and Anthony Macula. Investigation of a Sybr-Green-Based Method to Validate DNA Sequences for DNA Computing. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada435503.

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Wada, Yasutaka. Working Paper PUEAA No. 3. Parallel Processing and Parallelizing Compilation Techniques for "Green Computing". Universidad Nacional Autónoma de México, Programa Universitario de Estudios sobre Asia y África, 2022. http://dx.doi.org/10.22201/pueaa.001r.2022.

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Abstract:
The fourth technological revolution has brought great advances in manufacturing processes and human communications. Although processors have become increasingly efficient, both in speed, capacity and energy consumption, their functionality regarding this last point has yet to improve. The latest innovations represent an opportunity to create "green computing" and not only more environmentally friendly electronics and software, but also to use their new efficiency to improve our daily activities, as well as the designs of our cities themselves to make them more environmentally sustainable. These new computerized systems must also be applied in accordance with the socioeconomic factors that must be taken into account in order to be modified in favor of sustainability and efficiency.
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Brian, Dominikus. Next-Generation Smart-City Energy and Resource Management Powered by Exascale Green Computing Infrastructure and Artificial Intelligence System. ResearchHub Technologies, Inc., May 2024. http://dx.doi.org/10.55277/researchhub.rv1s6td0.

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Corum, Zachary, Ethan Cheng, Stanford Gibson, and Travis Dahl. Optimization of reach-scale gravel nourishment on the Green River below Howard Hanson Dam, King County, Washington. Engineer Research and Development Center (U.S.), April 2022. http://dx.doi.org/10.21079/11681/43887.

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The US Army Corps of Engineers, Seattle District, nourishes gravel downstream of Howard Hanson Dam (HHD) on the Green River in Washington State. The study team developed numerical models to support the ongoing salmonid habitat improvement mission downstream of HHD. Recent advancements in computing and numerical modeling software make long-term simulations in steep, gravel, cobble, and boulder river environments cost effective. The team calibrated mobile-bed, sediment-transport models for the pre-dam and post-dam periods. The modeling explored geomorphic responses to flow and sediment regime changes associated with HHD construction and operation. The team found that pre-dam conditions were significantly more dynamic than post-dam conditions and may have had lower spawning habitat quality in the project vicinity. The team applied the Bank Stability and Toe Erosion Model to the site and then calibrated to the post-dam gravel augmentation period. The team implemented a new hiding routine in HEC-RAS that improved the simulated grain size trends but underestimated coarse sediment transport. Models without the hiding function overestimated grain size but matched bed elevations and mass flux very well. Decade-long simulations of four future gravel nourishment conditions showed continued sediment storage in the reach. The storage rate was sensitive to nourishment mass and grain size.
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