Green Cloud Computing Research Articles

Building a Greener Future : The Role of Data Centers in Eco-Friendly Computing

This comprehensive article explores the critical issue of environmental sustainability in the rapidly evolving landscape of information technology, focusing on the substantial environmental impact of data centers. It delves into innovative strategies and technologies to create more sustainable and eco-friendly computing environments. The article covers key areas such as energy efficiency, renewable energy integration, green cloud computing, edge computing, circular economy practices, and AI-powered optimization. By examining these aspects, the article demonstrates how the IT industry is working to minimize its environmental footprint while meeting the growing demands of our digital world. It highlights not only the environmental benefits of these initiatives but also their economic advantages, showcasing how sustainable practices in computing can lead to significant cost savings and competitive advantages in the market.

Minimizing Carbon Emissions by Improving Water and Energy Use Efficiencies in AI Servers: A Green Cloud Computing Strategy for Sustainable Artificial Intelligence Systems

The advent of Artificial Intelligence systems, in particular of generative models like ChatGPT, has resulted in one more area requiring heavy computational resources which in turn consumes a lot of energy and water. By estimations, one interaction with ChatGPT for instance will take an estimate of 2.9 watt hour, which is ten times higher than the amount of energy consumed to conduct an ordinary googling task that is 0.3 watt hours. This stands as a call for action toward improving the water to energy ratio of the AI systems and therefore the recent carbon emissions. This paper explores the energy efficiency patterns of AI languages such as chatbots compared with the other means of searching the internet like Google and how the effects of the AI machines on the environment can be reduced. In this connection, green cloud computing methods have been suggested as possible solutions that can be effectively combined with the principles of clean energy use; on this list are both advanced systems for maintaining low temperatures and the optimization of AI systems. Finally, using of resources could also play a crucial part in the ultimate decrease in the adverse effects that the AI industry has on our environment.

A succinct state-of-the-art survey on green cloud computing: Challenges, strategies, and future directions

Cloud computing is a method of providing various computing services, including software, hardware, databases, data storage, and infrastructure, to the public through the Internet. The rapid expansion of cloud computing services has raised significant concerns over their environmental impact. Cloud computing services should be designed in a green manner, efficient in energy consumption, virtualized, consolidated, and eco-friendly. Green Cloud Computing (GCC) is a significant field of study that focuses on minimizing the environmental impact and energy usage of cloud infrastructures. This survey provides a comprehensive overview of the current state of GCC, focusing on the challenges, strategies, and future directions. The review study begins by identifying important challenges in GCC from practical implementations, identifying GCC-introduced environmental protection and prevention initiatives, and expressing the demand for long-term technical progression. It then addresses GCC’s primary concerns, such as energy efficiency, resource management, operational costs, and carbon emissions, and categorizes implementations according to algorithms, architectures, frameworks, general issues, and models and methodologies. Furthermore, enhancements in virtualization, multi-tenancy, and consolidation have been identified, analyzed, and accurately portrayed to address the advancements in GCC. Finally, the survey outlines future research directions and opportunities for advancing the field of GCC, including the development of novel algorithms, technologies for energy harvesting, and energy-efficient and eco-friendly solutions. By providing a comprehensive overview of GCC, this survey aims to serve as documentation for further evolving new emerging technological approaches in the GCC environment.

Eco-FL: Enhancing Federated Learning sustainability in edge computing through energy-efficient client selection

In the realm of edge cloud computing (ECC), Federated Learning (FL) revolutionizes the decentralization of machine learning (ML) models by enabling their training across multiple devices. In this way, FL preserves privacy and minimizes the need for centralized data by processing data near the source. From a communication standpoint, only the model weights are exchanged between devices. By avoiding the need to send data to a centralized location for processing, FL reduces the energy required for data transfer and supports more efficient use of computing resources at the edge. FL is particularly advantageous for resource-constrained devices, such as smartphones and IoT devices. However, this limited computational power and battery capacity and the challenge of energy consumption are critical aspects of FL systems. This paper introduces Eco-FL, an innovative methodology designed to optimize energy consumption in FL systems, in the field of Green Edge Cloud Computing (GECC). Our approach employs a device selection process that considers the entropy of the data held by the devices and their available energy reserves. This ensures that devices with lower energy availability are less likely to participate in the training rounds, prioritizing those with higher energy capacities. To evaluate the efficacy of our methodology, we utilize FedEntropy, an entropy-based aggregation method, alongside established aggregation methods such as FedAvg and FedProx for performance comparison. The effectiveness of Eco-FL in reducing energy consumption without compromising the accuracy of the FL process is demonstrated through analyses conducted on three distinct datasets. These analyses vary the β parameter of the Dirichlet distribution and account for scenarios with both homogeneous and heterogeneous initial device charges. Our findings validate Eco-FL’s potential to enhance the sustainability of FL systems by judiciously managing client participation based on energy criteria, presenting a significant step forward in the development of energy-efficient FL.

Unveiling Genetic Reinforcement Learning (GRLA) and Hybrid Attention-Enhanced Gated Recurrent Unit with Random Forest (HAGRU-RF) for Energy-Efficient Containerized Data Centers Empowered by Solar Energy and AI

The adoption of renewable energy sources has seen a significant rise in recent years across various industrial sectors, with solar energy standing out due to its eco-friendly characteristics. This shift from conventional fossil fuels to solar power is particularly noteworthy in energy-intensive environments such as cloud data centers. These centers, which operate continuously to support active servers via virtual instances, present a critical opportunity for the integration of sustainable energy solutions. In this study, we introduce two innovative approaches that substantially advance data center energy management. Firstly, we introduce the Genetic Reinforcement Learning Algorithm (GRLA) for energy-efficient container placement, representing a pioneering approach in data center management. Secondly, we propose the Hybrid Attention-enhanced GRU with Random Forest (HAGRU-RF) model for accurate solar energy prediction. This model combines GRU neural networks with Random Forest algorithms to forecast solar energy production reliably. Our primary focus is to evaluate the feasibility of solar energy in meeting the energy demands of cloud data centers that utilize containerization for virtualization, thereby promoting green cloud computing. Leveraging a robust German photovoltaic energy dataset, our study demonstrates the effectiveness and adaptability of these techniques across diverse environmental contexts. Furthermore, comparative analysis against traditional methods highlights the superior performance of our models, affirming the potential of solar-powered data centers as a sustainable and environmentally responsible solution.

Green Cloud Computing: Innovating for Sustainable, Low-Impact Data Management.

Cloud computing is the internet-based provision of computer services. The usage of some cloud services by individuals and businesses is expanding quickly. The rising demand for cloud infrastructure has led to a notable rise in the energy consumption of data centers. Reducing energy usage is essential to lowering carbon dioxide emissions, hence it must be addressed. Given that green cloud computing's main objective is to maximize energy efficiency, it is acknowledged as a potential solution to this issue. This paper presents green cloud computing alternatives for energy savings in addition to defining cloud computing with the key cloud service models, deployment strategies, and computing platforms. After that, these solutions were categorized based on how well they reduced energy.

A Survey on Green Cloud Computing

The future of the IT industry is at the crossroads, and unless there is no sustainable solution formulated within the industry, it would potentially be the end of the planet. Data centers within the industry consume most of the energy, and there is an immediate need for these power centers to switch towards green and clean energy. This study investigates the dynamics of Green Cloud Computing services and performs a literature review to examine the need, barriers, and trends of green cloud computing. By investigating the characteristics, barriers, and trends, the study proposes that the future of IT is deeply entrenched with green energy. The results of the study note that green cloud computing can be highly effective in enhancing the benefits of cloud computing further and mitigating its effects on the environment.

A systematic review of green-aware management techniques for sustainable data center

Cloud computing is one of the powerful engines driving global industrial upgrading and the booming digital economy. However, the explosive growth of cloud data centers (DCs) has resulted in inevitable energy consumption and carbon emission problems. Therefore, constructing energy-efficient and sustainable DCs will be essential for green cloud computing. This review makes several efforts to thoroughly investigate and track the research progress and routes to sustainable DCs. Firstly, we construct a new conceptual model of sustainable DCs to cover cutting-edge research results and indicate future evolutionary directions. Secondly, this review provides a comprehensive survey of five topics from a technical perspective: workload management, virtual resource management, energy management, thermal management, and waste heat recovery. Subsequently, some real-world datasets relevant to the topics, including workload traces, renewable energy data, and electricity price traces, have been specifically collected to support researchers in conducting further research. Finally, based on observations of existing works, we highlight some salient technical challenges and promising solutions to provide sensible energy and carbon reduction suggestions in sustainable DCs.

A Neighborhood Inspired Multiverse Scheduler for Energy and Makespan Optimized Task Scheduling for Green Cloud Computing Systems

A Neighborhood Inspired Multiverse Scheduler for Energy and Makespan Optimized Task Scheduling for Green Cloud Computing Systems

Data Centre Efficiency Enhancement by Metrics Oriented Approach to Revamp Green Cloud Computing Concept

Cloud computing inherits sharing of data from pool of resources existing in data centres when ever demanded. The imminent requirement for this purpose is proficiency of the data centre for fulfilment of this coveted objective. The pursuit of energy-efficient peak performance level is challenged by a simultaneous hike of energy consumption. The energy-efficient metrics contribute a major role for attainment of desired objective of safeguarding the environment. These metrics address the enhancement of the system’s proficiency. An increased energy-efficiency results into reduced consumption of energy resources since these energy resources are mostly non-renewable in nature and are the main source of carbon and heat emissions from operational data centres. As a matter of fact, any individual metric is not capable of achieving enhanced energy-efficient performance in a data centre. Therefore a collective utilization of selected metrics pertaining to power, performance and network traffic can improve the energy-efficient capability of data centre communication systems. The testing platform for such metrics is based on certain architectures which include D Cell, B Cube, Hyper Cube and Fat tree three-tier architectures.

Optimization Techniques For Low Energy Consumption In Green Cloud Computing

Computing in the cloud can assist businesses in shifting their focus to the development of solid business applications that will bring about genuine value to the businesses. Green computing, often known as environmentally sustainable computing, is the definition of green computing. It is a reference to the efforts that are made to maximise the usage of power consumption & energy efficiency while simultaneously minimising the cost & the amount of CO2 emission. To conduct a study on optimisation techniques & procedures that assist us in optimising low energy consumption & evaluating multiple parameters in order to obtain the desired output is the primary purpose of this research. Energy-Conscious Multisite Computation Offloading Techniques (EMOGC) for Green Cloud Computing is the methodology that was utilised throughout this project. Simulation & analysis are presented in The Energy-Conscious Multisite Computation Offloading Techniques for Green Cloud Computing in order to investigate time-efficient scheduling on multisite, which is responsible for optimising energy, time, & cost at the optimum time. This strategy seeks to finish the application within the allotted amount of time while also consuming as little power as feasible from the connected devices. According to the findings of this research, it is clear that the explored technique is effective in obtaining high throughput (HT) while simultaneously minimising the execution time, which in turn enhances the data rate in Green Cloud Computing (GCC).

Report on Current Trends and Future Research Challenges in Green Cloud Computing

Abstract: Cloud computing provides computing power and resources as a service to users across the globe. This scheme was introduced as a means to an end for customer’s worldwide, providing high performance at a cheaper cost when compared to dedicated high-performance computing machines. This provision requires huge data-centers to be tightly-coupled with the system, the increasing use of which yields heavy consumption of energy and huge emission of CO2. Since energy has been a prime concern of late, this issue generated the importance of green cloud computing that provides techniques and algorithms to reduce energy wastage by incorporating its reuse. In this survey we discuss key techniques to reduce the energy consumption and CO2 emission that can cause severe health issues. We begin with a discussion on green matrices appropriate for data-centers and then throw light on green scheduling algorithms that facilitate reduction in energy consumption and CO2 emission levels in the existing systems. At the same time the various existing architectures related to green cloud also discussed in this paper with their pros and cons.

LOAD BALANCING IN DATA CENTRE OF CLOUD COMPUTING

The internet is expanding its viewpoint into each conceivable part of the cutting-edge economy. Unshackled from our web programs programs today, the internet is characterizing our way of life, regardless of whether it’s sitting in front of the TV or driving an independent car. Cloud computing which is an on violently expanded our computerized lives, through, there have been critical improvement as far as accessibility fluc-tution, time and quality in administration are concerned, the unbounded development of our computerized way of life requires monstrous measures of power especially for the data centres that fill in as the mind of the advanced economy. All things considered, the move by clients toward cloud, will exceeding any energy productivity increase, which has record for wer 70% os data centre development in 2018. Many research advancements are already made in this domain for minimizing the energy utilization of the computing types of gear included; for effificient power energy consumption, decrease of carbon impression and e-squander. These procedures are supporters of green cloud computing, which are focused on planning and advancing energy-profificient activities to contain inordinate energy utilization in data centers. Cloud computing is a commercial and economic paradigm that has gained traction since 2006 and is presently the most signifificant technology in IT sector. From the notion of cloud computing to its energy effificiency, cloud has been the subject of much discussion. The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030. The data centres require a lot of power to provide services, which increases CO2 emissions. In this survey paper, software based technologies that can be used for building green data centers and include power management at individual software level has been discussed. The paper discusses the energy effificiency in containers and problem-solving approaches used for reducing power consumption in data centers. The cloud computing revolution is redesigning modern networking, and offering promising environmentalprotection prospects as well as economic and technological advantages. These technologies have the potential to improve energy effificiency and to reduce carbon footprints and (e- )waste. These features can transform cloud computing into green cloud computing. In this survey, we review the main achievements of green cloud computing. First,an overview of cloud computing is given.Then, recent studies and developments are summarized, and environmental issues are specififically addressed. Finally, future research directions and open problems regarding green cloud computing are presented. This survey is intended to serve asup-to-date guidance for research with respect to green cloud computing. Key Words: Server, Memory, Data center, Load balancing, Workload categorization, Energy effificiency ,Green information

Designing digital health applications for climate change mitigation and adaptation.

Designing digital health applications for climate change mitigation and adaptation.

A green scheduling algorithm for cloud-based honeynets

Modern businesses leverage cloud architecture to achieve agile and cost-effective technology services. Doing so comes at the expense of the environment though cloud technologies consume large quantities of energy. Cloud energy consumption is concerning in light of global climate trends and dwindling fossil fuel reserves. Consequently, increasing attention is given to sustainable and green cloud computing, which seeks to optimize compute-resource allocation and usage of virtualized systems and services. At the same time, progress toward sustainable and green cloud technology is impeded because as more enterprises deploy services into cloud architecture, cybersecurity threats follow. Unfortunately, cybersecurity technologies are optimized for maximum service overwatch without regard for compute resources and energy. This negates the energy reduction achieved in recent sustainable technology advancements. In this work, a generalized cybersecurity honeynet scheduling algorithm is proposed, in which power, CPU, and network overhead are operationalized to increase sustainability while balancing defensive mechanisms. The work describes both the mathematical foundation for the algorithm and a pseudocode proof of concept.

A Novel Energy and Communication Aware Scheduling on Green Cloud Computing

A Novel Energy and Communication Aware Scheduling on Green Cloud Computing

Machine Learning perspectives

The Journal of Smart Environments and Green Computing is an international, peer-reviewed, open access journal which provides a forum for the publication of papers addressing the variety of theoretical, methodological, epistemological, empirical and practical issues. The following topics are especially welcome: green computing, sustainable computing, energy efficiency, decision making, green cloud computing, smart cities, renewable energy, smart environments, etc.

Shifting from Cloud Computing to Green Cloud and Edge Computing

Shifting from Cloud Computing to Green Cloud and Edge Computing

Energy efficient fault tolerance techniques in green cloud computing: A systematic survey and taxonomy

Cloud computing has brought the accessibility of several software platforms under a single roof. It has transformed resources into scalable services on demand and provides the only solution to the high resource requirements. All cloud service providers usually offer all types of services in the cloud computing environment, even though they also handle security-related challenges like reliability, availability, and throughput. One of the most decisive challenges in the cloud is handling faults. High fault tolerance in the cloud is a must to attain high performance, and the defects must be investigated and examined for future guidance. The principal target of this paper is to gain insight into the fault tolerance techniques that are available to us and the challenges that are required to be overcome. We concluded that there is always a relation between faults and energy consumption during our survey. If there is a high potential to tolerate a fault, there will be a need for more infrastructure and devices to fix those faults, which further leads to more power consumption. In this paper, 129 Research papers published through February 2022 were considered and further classified. This paper critically reviews techniques to toleratefaultsin cloud computing systems and discusses the taxonomy of errors, faults, and failures. Furthermore, this paper aims to investigate several critical research topics and advanced techniques, such as artificial intelligence, deep learning, the Internet of Things, and machine learning, that may be employed as an intelligent fault tolerance strategy in the cloud environment.

A Review of Green Environment Cloud Computing

Green computing is a way of doing computing that helps the environment by minimizing the amount of energy needed by a computer, server, or device, using recyclable or biodegradable materials, and producing less trash when making products. Computer usage is increasing quickly, which increases the amount of carbon dioxide in the environment. Green technology methods can helpin constructing a secure environment for us to live in. Because of the rising popularity of green technologies, green cloud computing is a well-known broad subject and a popular field for researchtremendous computing and data storage needs. A supreme and astounding method for virtualizing servers and data centers and making them energy-efficient has been developed by cloud computing.In order to take use of various IT resources, virtualizing servers and data centers and making them energy efficient is now possible thanks to cloud computing. IT resources use a lot of electricity and energy, which causes an energy crisis and a change in the climate of the earth. Green cloud computing is therefore required since it may produce solutions that not only make IT resources energy efficient but also lower operational costs. Green IT is recognized as a crucial step in the IT industry's attempt to address environmental issues.